Paper Digest: CIKM 2017 Highlights
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.
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Paper Digest Team
TABLE 1: CIKM 2017 Papers
|Machine Learning @ Amazon
|I will then talk about three specific applications where we use a variety of methods to learn semantically rich representations of data: question answering where we use deep learning techniques, product size recommendations where we use probabilistic models, and fake reviews detection where we use tensor factorization algorithms.
|Deception Detection: When Computers Become Better than Humans
|In this talk, I will describe our work in building linguistic and multimodal algorithms for deception detection, targeting deceptive statements, trial videos, fake news, identity deceptions, and also going after deception in multiple cultures.
|When Deep Learning Meets Transfer Learning
|In this talk, I will give an overview of how transfer learning can help alleviate these problems.
|A Hyper-connected World
|K. Ananth Krishnan
|A Hyper-connected World
|Jointly Modeling Static Visual Appearance and Temporal Pattern for Unsupervised Video Hashing
|Chao Li, Yang Yang, Jiewei Cao, Zi Huang
|In this paper, we propose to jointly model static visual appearance and temporal pattern for video hash code generation, as both of them are believed to be carrying important information for learning an effective hash function.
|Construction of a National Scale ENF Map using Online Multimedia Data
|Hyunsoo Kim, Youngbae Jeon, Ji Won Yoon
|In this paper, we proposed a novel approach to constructing the worldwide ENF map by analyzing streaming data obtained by online multimedia services, such as "Youtube", "Earthcam", and "Ustream" instead of expensive specialized hardware.
|Dual Learning for Cross-domain Image Captioning
|Wei Zhao, Wei Xu, Min Yang, Jianbo Ye, Zhou Zhao, Yabing Feng, Yu Qiao
|In this paper, we propose a cross-domain image captioning approach that uses a novel dual learning mechanism to overcome this barrier.
|A New Approach to Compute CNNs for Extremely Large Images
|Sai Wu, Mengdan Zhang, Gang Chen, Ke Chen
|In this paper, we propose a new approach that adopts the BSP (bulk synchronization parallel) model to compute CNNs for images of any size.
|Active Sampling for Large-scale Information Retrieval Evaluation
|Dan Li, Evangelos Kanoulas
|In this paper we seek to solve the problem of large-scale retrieval evaluation combining the two approaches.
|Intent Based Relevance Estimation from Click Logs
|Prakash Mandayam Comar, Srinivasan H. Sengamedu
|In this paper, we outline a technique to model the interplay of query, user intent and position bias with respect to the relevance of the retrieved search results.
|A Comparison of Nuggets and Clusters for Evaluating Timeline Summaries
|Gaurav Baruah, Richard McCreadie, Jimmy Lin
|In this paper, by building test collections that have both nugget and cluster annotations, we are able to compare these two approaches.
|Sensitive and Scalable Online Evaluation with Theoretical Guarantees
|Harrie Oosterhuis, Maarten de Rijke
|Our contribution is two-fold.
|Users Are Known by the Company They Keep: Topic Models for Viewpoint Discovery in Social Networks
|Thibaut Thonet, Guillaume Cabanac, Mohand Boughanem, Karen Pinel-Sauvagnat
|To address this task, we propose in this paper a novel unsupervised topic model, the Social Network Viewpoint Discovery Model (SNVDM).
|Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network
|Jiajun Cheng, Shenglin Zhao, Jiani Zhang, Irwin King, Xin Zhang, Hui Wang
|To solve this problem, we propose a HiErarchical ATtention (HEAT) network for aspect-level sentiment classification.
|Dyadic Memory Networks for Aspect-based Sentiment Analysis
|Yi Tay, Luu Anh Tuan, Siu Cheung Hui
|This paper proposes Dyadic Memory Networks (DyMemNN), a novel extension of end-to-end memory networks (memNN) for aspect-based sentiment analysis (ABSA).
|Modeling Language Discrepancy for Cross-Lingual Sentiment Analysis
|Qiang Chen, Chenliang Li, Wenjie Li
|In this paper, we aim to model the language discrepancy in sentiment expressions as intrinsic bilingual polarity correlations (IBPCs) for better cross-lingual sentiment analysis.
|Multi-view Clustering with Graph Embedding for Connectome Analysis
|Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin
|To solve this problem, in this paper we propose a Multi-view Clustering framework on graph instances with Graph Embedding (MCGE).
|Attributed Signed Network Embedding
|Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu
|Therefore, in this paper, we study the novel problem of signed social network embedding with attributes.
|Enhancing the Network Embedding Quality with Structural Similarity
|Tianshu Lyu, Yuan Zhang, Yan Zhang
|We present a new method, SNS, that performs network embeddings using structural information (namely graphlets) to enhance its quality.
|On Embedding Uncertain Graphs
|Jiafeng Hu, Reynold Cheng, Zhipeng Huang, Yixang Fang, Siqiang Luo
|To tackle these problems, we propose a solution called URGE, or UnceRtain Graph Embedding.
|A Large Scale Prediction Engine for App Install Clicks and Conversions
|Narayan Bhamidipati, Ravi Kant, Shaunak Mishra
|In this paper, we describe (a) how we built a scalable machine learning pipeline from scratch to predict the probability of users clicking and installing apps in response to ad impressions, (b) the novel features we developed to improve our model performance, (c) the training and scoring pipelines that were put into production, (d) our A/B testing process along with the metrics used to determine significant improvements, and (e) the results of our experiments.
|Building Natural Language Interfaces to Web APIs
|Yu Su, Ahmed Hassan Awadallah, Madian Khabsa, Patrick Pantel, Michael Gamon, Mark Encarnacion
|We propose a novel approach to collect training data for NL2API via crowdsourcing, where crowd workers are employed to generate diversified NL commands.
|UFeed: Refining Web Data Integration Based on User Feedback
|Ahmed El-Roby, Ashraf Aboulnaga
|In this paper, we introduce UFeed, a system that refines relational mediated schemas and mappings based on user feedback over query answers.
|Extracting Records from the Web Using a Signal Processing Approach
|Roberto Panerai Velloso, Carina F. Dorneles
|We present here a novel approach, fully automatic and computationally efficient, using signal processing techniques to detect regularities and patterns in the structure of web pages.
|A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases
|Akshay Kansal, Francesca Spezzano
|In this paper, we propose a new index based on graph-coarsening to speed up subgraph query answering time in dynamic graph databases.
|Natural Language Question/Answering: Let Users Talk With The Knowledge Graph
|Weiguo Zheng, Hong Cheng, Lei Zou, Jeffrey Xu Yu, Kangfei Zhao
|In this paper, we present a data + oracle approach to answer NLQs over knowledge graphs.
|Keyword Search on RDF Graphs – A Query Graph Assembly Approach
|Shuo Han, Lei Zou, Jeffery Xu Yu, Dongyan Zhao
|In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm.
|Region Representation Learning via Mobility Flow
|Hongjian Wang, Zhenhui Li
|In this paper, we are interested in learning vector representations for regions using the large-scale taxi flow data.
|Learning Visual Features from Snapshots for Web Search
|Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, Liang Pang, Xueqi Cheng
|In this work, we propose to learn rich visual features automatically from the layout of Web pages (i.e., Web page snapshots) for relevance ranking.
|DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval
|Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Jingfang Xu, Xueqi Cheng
|In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process.
|Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities
|Asia J. Biega, Azin Ghazimatin, Hakan Ferhatosmanoglu, Krishna P. Gummadi, Gerhard Weikum
|In this paper, we propose the first model for quantifying search exposure on the service provider side, casting it into a reverse k-nearest-neighbor problem.
|Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
|Harrie Oosterhuis, Maarten de Rijke
|Our contribution is twofold.
|Crowd-enabled Pareto-Optimal Objects Finding Employing Multi-Pairwise-Comparison Questions
|Chang Liu, Yinan Zhang, Lei Liu, Lizhen Cui, Dong Yuan, Chunyan Miao
|To address this issue, we propose an algorithm, which uses preference relations given by crowdsourcing, to find Pareto-optimal objects with shorter latency and lower monetary costs.
|Destination-aware Task Assignment in Spatial Crowdsourcing
|Yan Zhao, Yang Li, Yu Wang, Han Su, Kai Zheng
|In this paper we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks.
|Crowdsourced Selection on Multi-Attribute Data
|Xueping Weng, Guoliang Li, Huiqi Hu, Jianhua Feng
|To address this problem, we propose predicate order based framework to reduce monetary cost.
|Select Your Questions Wisely: For Entity Resolution With Crowd Errors
|Vijaya Krishna Yalavarthi, Xiangyu Ke, Arijit Khan
|Based on detailed empirical analysis over real-world datasets, we find that our proposed solution, PERC (probabilistic entity resolution with imperfect crowd) improves the quality by 15% and reduces the overall cost by 50% for the crowdsourcing-based entity resolution.
|Reply With: Proactive Recommendation of Email Attachments
|Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi, Roy Rosemarin, Grzegorz Kukla, Piotr Grudzien, Nicola Cancedda
|In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user.
|Learning and Transferring Social and Item Visibilities for Personalized Recommendation
|Lin Xiao, Zhang Min, Zhang Yongfeng, Liu Yiqun, Ma Shaoping
|In this paper, we propose a novel user preference model for recommender systems that considers the visibility of both items and social relationships.
|Joint Topic-Semantic-aware Social Recommendation for Online Voting
|Hongwei Wang, Jia Wang, Miao Zhao, Jiannong Cao, Minyi Guo
|In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation.
|Interactive Social Recommendation
|Xin Wang, Steven C.H. Hoi, Chenghao Liu, Martin Ester
|In the real world, new users may leave the systems for the reason of being recommended with boring items before enough data is collected for training a good model, which results in an inefficient customer retention.
|From Properties to Links: Deep Network Embedding on Incomplete Graphs
|Dejian Yang, Senzhang Wang, Chaozhuo Li, Xiaoming Zhang, Zhoujun Li
|In this paper, we for the first time study the problem of network embedding on incomplete networks.
|Learning Community Embedding with Community Detection and Node Embedding on Graphs
|Sandro Cavallari, Vincent W. Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria
|In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead of each individual nodes.
|Attributed Network Embedding for Learning in a Dynamic Environment
|Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu
|In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework – DANE.
|Learning Node Embeddings in Interaction Graphs
|Yao Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu
|In this paper, we study the problem of node embedding in attributed interaction graphs.
|Efficient Computation of Subspace Skyline over Categorical Domains
|Md Farhadur Rahman, Abolfazl Asudeh, Nick Koudas, Gautam Das
|In this paper, we place the problem of skyline discovery over categorical attributes into perspective and design efficient algorithms for two cases.
|Fast Algorithms for Pareto Optimal Group-based Skyline
|Wenhui Yu, Zheng Qin, Jinfei Liu, Li Xiong, Xu Chen, Huidi Zhang
|To address this gap, we study the skyline computation in group case and propose fast methods to find the group-based skyline (G-skyline), which contains Pareto optimal groups.
|Probabilistic Skyline on Incomplete Data
|Kaiqi Zhang, Hong Gao, Xixian Han, Zhipeng Cai, Jianzhong Li
|In this paper, we propose a novel skyline definition utilizing probabilistic model on incomplete data where each point has a probability to be in the skyline.
|Communication-Efficient Distributed Skyline Computation
|Haoyu Zhang, Qin Zhang
|In this paper we study skyline queries in the distributed computational model, where we have s remote sites and a central coordinator; each site holds a piece of data, and the coordinator wants to compute the skyline of the union of the s datasets.
|Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
|Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal
|We describe the overall design and architecture of the statistical modeling system underlying this product.
|Efficient Document Filtering Using Vector Space Topic Expansion and Pattern-Mining: The Case of Event Detection in Microposts
|Julia Proskurnia, Ruslan Mavlyutov, Carlos Castillo, Karl Aberer, Philippe Cudré-Mauroux
|In this paper, we propose a robust and effective approach to automatically identify microposts related to a specific topic defined by a small sample of reference documents.
|LARM: A Lifetime Aware Regression Model for Predicting YouTube Video Popularity
|Changsha Ma, Zhisheng Yan, Chang Wen Chen
|In this paper, we aim to achieve fast prediction of long-term video popularity in the complex YouTube networks.
|Modeling Affinity based Popularity Dynamics
|Minkyoung Kim, Daniel A. McFarland, Jure Leskovec
|In this study, we propose the Affinity Poisson Process model (APP) which models popularity dynamics, by incorporating (1) affinities between subgroups, (2) heterogeneous preferential attachment, and (3) subgroup-level time decay.
|Scenic Routes Now: Efficiently Solving the Time-Dependent Arc Orienteering Problem
|Ying Lu, Gregor Josse, Tobias Emrich, Ugur Demiryurek, Matthias Renz, Cyrus Shahabi, Matthias Schubert
|Therefore, we propose an efficient approximate solution with spatial pruning techniques, optimized for fast response systems. In this paper, we introduce a novel problem called Twofold Time-Dependent Arc Orienteering Problem (2TD-AOP), which seeks to find a path from a source to a destination maximizing an accumulated value (e.g., attractiveness of the path) while not exceeding a cost budget (e.g., total travel time).
|Modeling Temporal-Spatial Correlations for Crime Prediction
|Xiangyu Zhao, Jiliang Tang
|In this paper, we exploit temporal-spatial correlations in urban data for crime prediction.
|Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data
|Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu, Naren Ramakrishnan
|To handle missing values, we propose a data tensor completion method based on price domain knowledge.
|Exploiting Spatio-Temporal User Behaviors for User Linkage
|Wei Chen, Hongzhi Yin, Weiqing Wang, Lei Zhao, Wen Hua, Xiaofang Zhou
|To tackle the problem, we propose a novel model STUL (Spatio-Temporal User Linkage) that consists of the following two components.
|Similarity-based Distant Supervision for Definition Retrieval
|Jiepu Jiang, James Allan
|We present a distant supervision approach addressing this challenge without using explicitly labeled data.
|Hybrid BiLSTM-Siamese network for FAQ Assistance
|Prerna Khurana, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan
|We describe an automated assistant for answering frequently asked questions; our system has been deployed, and is currently answering HR-related queries in two different areas (leave management and health insurance) to a large number of users.
|Regularized and Retrofitted models for Learning Sentence Representation with Context
|Tanay Kumar Saha, Shafiq Joty, Naeemul Hassan, Mohammad Al Hasan
|We evaluate our sentence representation models in a setup, where context is available to infer sentence vectors.
|Talking to Your TV: Context-Aware Voice Search with Hierarchical Recurrent Neural Networks
|Jinfeng Rao, Ferhan Ture, Hua He, Oliver Jojic, Jimmy Lin
|We tackle the novel problem of navigational voice queries posed against an entertainment system, where viewers interact with a voice-enabled remote controller to specify the TV program to watch.
|GPU-Accelerated Graph Clustering via Parallel Label Propagation
|Yusuke Kozawa, Toshiyuki Amagasa, Hiroyuki Kitagawa
|To this end, this paper proposes a fast graph clustering method using GPUs.
|Temporally Like-minded User Community Identification through Neural Embeddings
|Hossein Fani, Ebrahim Bagheri, Weichang Du
|We propose a neural embedding approach to identify temporally like-minded user communities, i.e., those communities of users who have similar temporal alignment in their topics of interest.
|Community-Based Network Alignment for Large Attributed Network
|Zheng Chen, Xinli Yu, Bo Song, Jianliang Gao, Xiaohua Hu, Wei-Shih Yang
|In this paper, based on Stochastic Block Model (SBM) and Dirichlet-multinomial, we propose "divide-and-conquer" models CAlign that jointly consider network alignment, community discovery and community alignment in one framework for large networks with node attributes, in an effort to reduce both the computation time and memory usage while achieving better or competitive performance.
|A Non-negative Symmetric Encoder-Decoder Approach for Community Detection
|Bing-Jie Sun, Huawei Shen, Jinhua Gao, Wentao Ouyang, Xueqi Cheng
|In this paper, we propose a non-negative symmetric encoder-decoder approach for community detection.
|Fast Word Recognition for Noise channel-based Models in Scenarios with Noise Specific Domain Knowledge
|Marco Cristo, Raíza Hanada, André Carvalho, Fernando Anglada Lores, Maria da Graça C. Pimentel
|In this work, we propose very efficient methods for word recognition in very noisy scenarios which support effective edit-based distance algorithms in a Mor-Fraenkel index, searchable using a minimum perfect hashing.
|Detecting Multiple Periods and Periodic Patterns in Event Time Sequences
|Quan Yuan, Jingbo Shang, Xin Cao, Chao Zhang, Xinhe Geng, Jiawei Han
|In this paper, we study the problem of discovering all true periods and the corresponded occurring patterns of an event from a noisy and incomplete observation sequence.
|Finding Periodic Discrete Events in Noisy Streams
|Abhirup Ghosh, Christopher Lucas, Rik Sarkar
|We describe a model of periodic events that covers both idealized and realistic scenarios characterized by multiple kinds of noise.
|Fast and Accurate Time Series Classification with WEASEL
|Patrick Schäfer, Ulf Leser
|In this paper, we present WEASEL (Word ExtrAction for time SEries cLassification), a novel TSC method which is both fast and accurate.
|QLever: A Query Engine for Efficient SPARQL+Text Search
|Hannah Bast, Björn Buchhold
|We present QLever, a query engine for efficient combined search on a knowledge base and a text corpus, in which named entities from the knowledge base have been identified (that is, recognized and disambiguated).
|A Study of Main-Memory Hash Joins on Many-core Processor: A Case with Intel Knights Landing Architecture
|Xuntao Cheng, Bingsheng He, Xiaoli Du, Chiew Tong Lau
|In this paper, we experimentally revisit the state-of-the-art main-memory hash join algorithms to study how the new hardware features of KNL affect the algorithmic design and tuning as well as to identify the opportunities for further performance improvement on KNL.
|PQBF: I/O-Efficient Approximate Nearest Neighbor Search by Product Quantization
|Yingfan Liu, Hong Cheng, Jiangtao Cui
|In this paper, we propose an I/O-efficient PQ based solution for ANN search.
|ANS-Based Index Compression
|Alistair Moffat, Matthias Petri
|Here we combine the recently developed "asymmetric numeral systems" (ANS) approach to entropy coding and a range of previous index compression methods, including VByte, Simple, and Packed.
|Covering the Optimal Time Window Over Temporal Data
|Bin Cao, Chenyu Hou, Jing Fan
|In this paper, we propose a new problem: covering the optimal time window over temporal data.
|Scaling Probabilistic Temporal Query Evaluation
|Melisachew Wudage Chekol
|In this work, we propose the PRATiQUE (PRobAbilistic Temporal QUery Evaluation) framework for scalable temporal query evaluation.
|Efficient Discovery of Abnormal Event Sequences in Enterprise Security Systems
|Boxiang Dong, Zhengzhang Chen, Hui (Wendy) Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li, Haifeng Chen
|In this work, we formulate a novel problem in intrusion detection – suspicious event sequence discovery, and propose GID, an efficient graph-based intrusion detection technique that can identify abnormal event sequences from massive heterogeneous process traces with high accuracy.
|Temporal Analog Retrieval using Transformation over Dual Hierarchical Structures
|Yating Zhang, Adam Jatowt, Katsumi Tanaka
|In this paper, we provide a general framework to bridge different domains across-time and, by this, to facilitate search and comparison as if carried in user’s familiar domain (i.e., the present).
|Does That Mean You’re Happy?: RNN-based Modeling of User Interaction Sequences to Detect Good Abandonment
|Kyle Williams, Imed Zitouni
|In this paper, we investigate how sequences of user interactions on the SERP differ between good and bad abandonment.
|Deep Sequential Models for Task Satisfaction Prediction
|Rishabh Mehrotra, Ahmed Hassan Awadallah, Milad Shokouhi, Emine Yilmaz, Imed Zitouni, Ahmed El Kholy, Madian Khabsa
|In this work we go beyond such atomic tasks and consider the problem of predicting user’s satisfaction when engaged in complex search tasks composed of many different queries and subtasks.
|Adaptive Persistence for Search Effectiveness Measures
|Jiepu Jiang, James Allan
|In contrast, we present work that adapts the persistence factor according to the ranking and relevance of the ranked lists being evaluated.
|Beyond Success Rate: Utility as a Search Quality Metric for Online Experiments
|Widad Machmouchi, Ahmed Hassan Awadallah, Imed Zitouni, Georg Buscher
|In this work, we propose the use of utility as a measure of searcher satisfaction.
|Linking News across Multiple Streams for Timeliness Analysis
|Ida Mele, Seyed Ali Bahrainian, Fabio Crestani
|In this paper, we propose techniques for cross-linking news streams based on the reported events with the purpose of analyzing the temporal dependencies among streams.
|Growing Story Forest Online from Massive Breaking News
|Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu
|We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion.
|iFACT: An Interactive Framework to Assess Claims from Tweets
|Wee Yong Lim, Mong Li Lee, Wynne Hsu
|In this work, we present an interactive framework called iFACT for assessing the credibility of claims from tweets.
|CSI: A Hybrid Deep Model for Fake News Detection
|Natali Ruchansky, Sungyong Seo, Yan Liu
|In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction.
|Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data
|Guansong Pang, Hongzuo Xu, Longbing Cao, Wentao Zhao
|This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data.
|Outlier Detection in Sparse Data with Factorization Machines
|Mengxiao Zhu, Charu C. Aggarwal, Shuai Ma, Hui Zhang, Jinpeng Huai
|In this study, we propose an outlier detection approach for sparse data with factorization machines.
|Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning
|Xian Teng, Yu-Ru Lin, Xidao Wen
|We propose a Multi-view Time-Series Hypersphere Learning (MTHL) approach that leverages multi-view learning and support vector description to tackle this problem.
|A Fast Trajectory Outlier Detection Approach via Driving Behavior Modeling
|Hao Wu, Weiwei Sun, Baihua Zheng
|Motivated by this, we propose a vehicle outlier detection approach namely DB-TOD which is based on probabilistic model via modeling the driving behavior/preferences from the set of historical trajectories.
|BL-ECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion
|Jiawei Zhang, Limeng Cui, Philip S. Yu, Yuanhua Lv
|In this paper, we propose to detect the social communities of the employees in companies based on the broad learning setting with both these online and offline information sources simultaneously, and the problem is formally called the "Broad Learning based Enterprise Community Detection" (BL-ECD) problem.
|Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks
|Tuan-Anh Hoang, Ee-Peng Lim
|In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks.
|To Be Connected, or Not to Be Connected: That is the Minimum Inefficiency Subgraph Problem
|Natali Ruchansky, Francesco Bonchi, David Garcia-Soriano, Francesco Gullo, Nicolas Kourtellis
|We study the problem of extracting a selective connector for a given set of query vertices Q subset of V in a graph G = (V,E).
|MGAE: Marginalized Graph Autoencoder for Graph Clustering
|Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang
|In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering.
|BoostVHT: Boosting Distributed Streaming Decision Trees
|Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales
|This paper introduces BoostVHT, a technique to parallelize online boosting algorithms.
|Stream Aggregation Through Order Sampling
|Nick Duffield, Yunhong Xu, Liangzhen Xia, Nesreen K. Ahmed, Minlan Yu
|This paper introduces a new single-pass reservoir weighted-sampling stream aggregation algorithm, Priority-Based Aggregation (PBA).
|FUSION: An Online Method for Multistream Classification
|Ahsanul Haque, Zhuoyi Wang, Swarup Chandra, Bo Dong, Latifur Khan, Kevin W. Hamlen
|In this paper, we propose an efficient solution for multistream classification by fusing drift detection into online data shift adaptation.
|Maintaining Densest Subsets Efficiently in Evolving Hypergraphs
|Shuguang Hu, Xiaowei Wu, T-H. Hubert Chan
|In this paper we study the densest subgraph problem, which plays a key role in many graph mining applications.
|Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services
|Yuqi Wang, Jiannong Cao, Lifang He, Wengen Li, Lichao Sun, Philip S. Yu
|In this work, we forecast order response time on current day by fusing data from order history and driver historical locations.
|Tensor Rank Estimation and Completion via CP-based Nuclear Norm
|Qiquan Shi, Haiping Lu, Yiu-ming Cheung
|Several Bayesian solutions have been proposed but they often under/over-estimate the tensor rank while being quite slow.
|Smart Infrastructure Maintenance Using Incremental Tensor Analysis: Extended Abstract
|Nguyen Lu Dang Khoa, Ali Anaissi, Yang Wang
|This work proposed a method called onlineCP-ALS to incrementally update tensor component matrices, followed by a self-tuning one-class support vector machine for online damage identification.
|Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems
|Ariyam Das, Ishan Upadhyaya, Xiangrui Meng, Ameet Talwalkar
|Using collaborative filtering as a case-study, we introduce an efficient model parallel industrial scale algorithm for alternating least squares (ALS), along with a highly optimized implementation of ALS that serves as the default implementation in MLlib, Apache Spark’s machine learning library.
|Modeling Student Learning Styles in MOOCs
|Yuling Shi, Zhiyong Peng, Hongning Wang
|In this work, based on a thorough qualitative study of students’ behaviors recorded in two MOOC courses with large student enrollments, we develop a non-parametric Bayesian model to capture students’ sequential learning activities in a generative manner.
|Tracking Knowledge Proficiency of Students with Educational Priors
|Yuying Chen, Qi Liu, Zhenya Huang, Le Wu, Enhong Chen, Runze Wu, Yu Su, Guoping Hu
|To this end, in this paper, we devise an explanatory probabilistic approach to track the knowledge proficiency of students over time by leveraging educational priors.
|Spreadsheet Property Detection With Rule-assisted Active Learning
|Zhe Chen, Sasha Dadiomov, Richard Wesley, Gang Xiao, Daniel Cory, Michael Cafarella, Jock Mackinlay
|In this paper, we focus on the problem of spreadsheet property detection.
|Learning Knowledge Embeddings by Combining Limit-based Scoring Loss
|Xiaofei Zhou, Qiannan Zhu, Ping Liu, Li Guo
|Learning Knowledge Embeddings by Combining Limit-based Scoring Loss
|Length Adaptive Recurrent Model for Text Classification
|Zhengjie Huang, Zi Ye, Shuangyin Li, Rong Pan
|In this paper, we propose a Length Adaptive Recurrent Model (LARM) which can automatically determine the minimum text length that is necessary to perform the classification.
|Multi-Task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs
|Yi Tay, Luu Anh Tuan, Minh C. Phan, Siu Cheung Hui
|In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting.
|Movie Fill in the Blank with Adaptive Temporal Attention and Description Update
|Jie Chen, Jie Shao, Fumin Shen, Chengkun He, Lianli Gao, Heng Tao Shen
|To address this problem, in this paper we propose to use a novel LSTM network called LSTM with Linguistic gate (LSTMwL), which exploits adaptive temporal attention for MovieFIB.
|Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media
|Rupinder Paul Khandpur, Taoran Ji, Steve Jan, Gang Wang, Chang-Tien Lu, Naren Ramakrishnan
|We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks.
|Budgeted Task Scheduling for Crowdsourced Knowledge Acquisition
|Tao Han, Hailong Sun, Yangqiu Song, Zizhe Wang, Xudong Liu
|In this paper, we present a new framework for task scheduling with the limited budget, targeting an effective solution to more specific knowledge acquisition.
|Hyper Questions: Unsupervised Targeting of a Few Experts in Crowdsourcing
|Jiyi Li, Yukino Baba, Hisashi Kashima
|In this paper, we focus on an important class of answer aggregation problems in which majority voting fails and propose the concept of hyper questions to devise effective aggregation methods.
|Modeling Menu Bundle Designs of Crowdfunding Projects
|Yusan Lin, Peifeng Yin, Wang-Chien Lee
|In this paper, we raise a novel research question: understanding project creators’ decisions of reward designs to level their chance to succeed.
|Forecasting Ad-Impressions on Online Retail Websites using Non-homogeneous Hawkes Processes
|Krunal Parmar, Samuel Bushi, Sourangshu Bhattacharya, Surender Kumar
|In this paper, we study the problem of predicting user visits or potential ad-impressions to online retail websites, based on historical time-stamps.
|Volume Ranking and Sequential Selection in Programmatic Display Advertising
|Yuxuan Song, Kan Ren, Han Cai, Weinan Zhang, Yong Yu
|In this paper, we borrow in the idea of top-N ranking and filtering techniques from information retrieval and propose an effective ad impression volume ranking method for each ad campaign, followed by a sequential selection strategy considering the remaining ad volume and budget, to smoothly deliver the volume filtering while maximizing campaign efficiency.
|On Migratory Behavior in Video Consumption
|Huan Yan, Tzu-Heng Lin, Gang Wang, Yong Li, Haitao Zheng, Depeng Jin, Ben Y. Zhao
|In this paper, we take a data-driven approach to analyze and model user migration behavior in video streaming, i.e., users switching content provider during active sessions.
|FM-Hawkes: A Hawkes Process Based Approach for Modeling Online Activity Correlations
|Sha Li, Xiaofeng Gao, Weiming Bao, Guihai Chen
|In this work, we target this new problem by modeling the interplay between the time series of different types of activities and apply our model to predict future user behavior.
|Deep Learning Based Forecasting of Critical Infrastructure Data
|Zahra Zohrevand, Uwe Glässer, Mohammad A. Tayebi, Hamed Yaghoubi Shahir, Mehdi Shirmaleki, Amir Yaghoubi Shahir
|In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods.
|Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information
|Wonsung Lee, Kyungwoo Song, Il-Chul Moon
|This paper presents variational approaches for collaborative filtering to deal with auxiliary information.
|DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades
|Qi Cao, Huawei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng
|In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.
|CNN-IETS: A CNN-based Probabilistic Approach for Information Extraction by Text Segmentation
|Meng Hu, Zhixu Li, Yongxin Shen, An Liu, Guanfeng Liu, Kai Zheng, Lei Zhao
|Information Extraction by Text Segmentation (IETS) aims at segmenting text inputs to extract implicit data values contained in them.The state-of-art IETS approaches mainly rely on machine learning techniques, either supervised or unsupervised.However, while the supervised approaches require a large labelled training data, the performance of the unsupervised ones could be unstable on different data sets.To overcome their weaknesses, this paper introduces CNN-IETS, a novel unsupervised probabilistic approach that takes the advantages of pre-existing data and a Convolution Neural Network (CNN)-based probabilistic classification model.
|A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection
|Zitao Liu, Milos Hauskrecht
|To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions.
|DiagTree: Diagnostic Tree for Differential Diagnosis
|Yejin Kim, Jingyun Choi, Yosep Chong, Xiaoqian Jiang, Hwanjo Yu
|We propose a Diagnostic Tree (DiagTree), a new framework for diagnosing diseases, which combines several tests to reduce the diagnosis time and to incorporate real-world constraints into discrete optimization.
|Fine-grained Patient Similarity Measuring using Deep Metric Learning
|Jiazhi Ni, Jie Liu, Chenxin Zhang, Dan Ye, Zhirou Ma
|In this paper, we present a novel three layer patient similarity deep metric learning framework (PSDML) by optimizing quadruple loss improved from triplet loss, to learn an embedding distance for disease classification among the patients.
|Differentially Private Regression for Discrete-Time Survival Analysis
|Thông T. Nguyên, Siu Cheung Hui
|In this work, we aim to propose solutions for the regression problem in survival analysis with the protection of differential privacy which is a golden standard of privacy protection in data privacy research.
|From Fingerprint to Footprint: Revealing Physical World Privacy Leakage by Cyberspace Cookie Logs
|Huandong Wang, Chen Gao, Yong Li, Zhi-Li Zhang, Depeng Jin
|In this paper we address the following fundamental question: what kind – and how much – of user physical world privacy might be leaked if we could get hold of such diverse network datasets even without any physical location information.
|Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition
|Lingjuan Lyu, Xuanli He, Yee Wei Law, Marimuthu Palaniswami
|For collaborative learning, we proposed a novel LSTM-CNN model combining the merits of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN).
|Privacy Aware Temporal Profiling of Emails in Distributed Setup
|Sutapa Mondal, Manish Shukla, Sachin Lodha
|In this paper, we propose a system for building an individual’s perceived knowledge profile "What she knows?" )
|Name Disambiguation in Anonymized Graphs using Network Embedding
|Baichuan Zhang, Mohammad Al Hasan
|In this work, we propose a novel name disambiguation method.
|Weakly-Guided User Stance Prediction via Joint Modeling of Content and Social Interaction
|Rui Dong, Yizhou Sun, Lu Wang, Yupeng Gu, Yuan Zhong
|In this work, we present a weakly-guided user stance modeling framework which simultaneously considers two types of information: what do you say (via stance-based content generative model) and how do you behave (via social interaction-based graph regularization).
|Social Media for Opioid Addiction Epidemiology: Automatic Detection of Opioid Addicts from Twitter and Case Studies
|Yujie Fan, Yiming Zhang, Yanfang Ye, Xin li, Wanhong Zheng
|In this paper, we propose a novel framework named AutoDOA to automatically detect the opioid addicts from Twitter, which can potentially assist in sharpening our understanding toward the behavioral process of opioid abuse and addiction.
|Understanding and Predicting Weight Loss with Mobile Social Networking Data
|Zhiwei Wang, Tyler Derr, Dawei Yin, Jiliang Tang
|In this paper, we conduct the initial investigation to understand weight loss with a large-scale mobile social networking dataset with near 10 million users.
|Tweet Geolocation: Leveraging Location, User and Peer Signals
|Wen-Haw Chong, Ee-Peng Lim
|We propose several models that leverage on three types of signals from locations, users and peers.
|A Two-step Information Accumulation Strategy for Learning from Highly Imbalanced Data
|Bin Liu, Min Zhang, Weizhi Ma, Xin Li, Yiqun Liu, Shaoping Ma
|In this paper, Our major point is that the imbalance is the observed phenomenon but not the cause of the problem.
|Understanding Database Performance Inefficiencies in Real-world Web Applications
|Cong Yan, Alvin Cheung, Junwen Yang, Shan Lu
|In this paper, we studied 27 real-world open-source applications built on top of the popular Ruby on Rails ORM framework, with the goal to understand the database-related performance inefficiencies in these applications.
|Data Driven Chiller Plant Energy Optimization with Domain Knowledge
|Hoang Dung Vu, Kok Soon Chai, Bryan Keating, Nurislam Tursynbek, Boyan Xu, Kaige Yang, Xiaoyan Yang, Zhenjie Zhang
|This paper presents our research and industrial experience on the adoption of data models and optimizations on chiller plant and discusses the lessons learnt from our practice on real world plants.
|Partitioning Orders in Online Shopping Services
|Sreenivas Gollapudi, Ravi Kumar, Debmalya Panigrahy, Rina Panigrahy
|Formulating this as an optimization problem, we propose a family of simple and efficient algorithms that admit natural constraints such as number of items a shopper can process in this setting.
|Taxonomy Induction Using Hypernym Subsequences
|Amit Gupta, Rémi Lebret, Hamza Harkous, Karl Aberer
|We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms.
|Unsupervised Concept Categorization and Extraction from Scientific Document Titles
|Adit Krishnan, Aravind Sankar, Shi Zhi, Jiawei Han
|Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.).
|MIKE: Keyphrase Extraction by Integrating Multidimensional Information
|Yuxiang Zhang, Yaocheng Chang, Xiaoqing Liu, Sujatha Das Gollapalli, Xiaoli Li, Chunjing Xiao
|In this paper, we focus on how to effectively exploit multidimensional information to improve the keyphrase extraction performance (MIKE).
|QALink: Enriching Text Documents with Relevant Q&A Site Contents
|Yixuan Tang, Weilong Huang, Qi Liu, Anthony K.H. Tung, Xiaoli Wang, Jisong Yang, Beibei Zhang
|In this paper, we devise a rigorous formulation of the novel text enrichment problem, and design an end-to-end system named QALink which assigns the most relevant Q&A contents to the corresponding section of the document.
|Sequence Modeling with Hierarchical Deep Generative Models with Dual Memory
|Yanan Zheng, Lijie Wen, Jianmin Wang, Jun Yan, Lei Ji
|In this paper, we propose a Hierarchical Deep Generative Model With Dual Memory to address the two challenges.
|Active Learning for Large-Scale Entity Resolution
|Kun Qian, Lucian Popa, Prithviraj Sen
|In this paper, we introduce an active learning system that learns, at scale, multiple rules each having significant coverage of the space of duplicates, thus leading to high recall, in addition to high-precision.
|Indexable Bayesian Personalized Ranking for Efficient Top-k Recommendation
|Dung D. Le, Hady W. Lauw
|In this paper, we introduce Indexable Bayesian Personalized Ranking (IBPR) that learns from ordinal preference to produce representation that is inherently compatible with the aforesaid indices.
|Latency Reduction via Decision Tree Based Query Construction
|Aman Grover, Dhruv Arya, Ganesh Venkataraman
|We present a way to model the underlying complex ranking function via decision trees.
|Broad Learning based Multi-Source Collaborative Recommendation
|Junxing Zhu, Jiawei Zhang, Lifang He, Quanyuan Wu, Bin Zhou, Chenwei Zhang, Philip S. Yu
|In this paper, we focus on studying the recommendation problem that can provide ratings of items or services.
|Neural Attentive Session-based Recommendation
|Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma
|In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem.
|A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation
|Jarana Manotumruksa, Craig Macdonald, Iadh Ounis
|In this paper, we propose a Deep Recurrent Collaborative Filtering framework (DRCF) with a pairwise ranking function that aims to capture user-venue interactions in a CF manner from sequences of observed feedback by leveraging Multi-Layer Perception and Recurrent Neural Network architectures.
|Recommendation with Capacity Constraints
|Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee
|Towards closing this gap, we propose Recommendation with Capacity Constraints — a framework that optimizes for both recommendation accuracy and expected item usage that respects the capacity constraints.
|Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources
|Yongfeng Zhang, Qingyao Ai, Xu Chen, W. Bruce Croft
|In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation.
|Interacting Attention-gated Recurrent Networks for Recommendation
|Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M.J. Tax
|In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.
|A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation
|Jarana Manotumruksa, Craig Macdonald, Iadh Ounis
|Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR.
|BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network
|Daizong Ding, Mi Zhang, Shao-Yuan Li, Jie Tang, Xiaotie Chen, Zhi-Hua Zhou
|In this paper, we propose a Bayesian Personalized Ranking Deep Neural Network (BayDNN) model for friend recommendation in social networks.
|A Topic Model Based on Poisson Decomposition
|Haixin Jiang, Rui Zhou, Limeng Zhang, Hua Wang, Yanchun Zhang
|Based on the validity of the test on a claim that the data conforms to Poisson distribution we propose Poisson decomposition model (PDM), a statistical model for modeling count data of text corpora, which can straightly capture each document’s multidimensional numerical characteristics on topics.
|A Matrix-Vector Recurrent Unit Model for Capturing Compositional Semantics in Phrase Embeddings
|Rui Wang, Wei Liu, Chris McDonald
|We present a novel recurrent computational mechanism that specifically learns the compositionality by encoding the compositional rule of each word into a matrix.
|Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
|Hosein Azarbonyad, Mostafa Dehghani, Kaspar Beelen, Alexandra Arkut, Maarten Marx, Jaap Kamps
|We propose an approach for detecting semantic shifts between different viewpoints—broadly defined as a set of texts that share a specific metadata feature, which can be a time-period, but also a social entity such as a political party.
|A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation
|Gaurav Singh, Iain J. Marshall, James Thomas, John Shawe-Taylor, Byron C. Wallace
|We propose a novel neural model that addresses these challenges.
|Sybil Defense in Crowdsourcing Platforms
|Dong Yuan, Guoliang Li, Qi Li, Yudian Zheng
|To address this problem, we propose a sybil defense framework for crowdsourcing, which can help crowdsourcing platforms to identify sybil workers and defense the sybil attack.
|HoloScope: Topology-and-Spike Aware Fraud Detection
|Shenghua Liu, Bryan Hooi, Christos Faloutsos
|Hence, we propose HoloScope, which introduces a novel metric "contrast suspiciousness" integrating information from graph topology and spikes to more accurately detect fraudulent users and objects.
|Building a Dossier on the Cheap: Integrating Distributed Personal Data Resources Under Cost Constraints
|Imrul Chowdhury Anindya, Harichandan Roy, Murat Kantarcioglu, Bradley Malin
|Thus, in this work, we investigate a novel privacy risk assessment framework, based on adversaries who plan an integration of datasets for the most accurate estimate of targeted sensitive attributes under a certain budget.
|DeMalC: A Feature-rich Machine Learning Framework for Malicious Call Detection
|Yuhong Li, Dongmei Hou, Aimin Pan, Zhiguo Gong
|In this work, we propose a solution named DeMalC to address those problems by applying the machine learning algorithmm on a novel set of discriminative features.
|FA*IR: A Fair Top-k Ranking Algorithm
|Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates
|In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n » k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria.
|Capturing Feature-Level Irregularity in Disease Progression Modeling
|Kaiping Zheng, Wei Wang, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, Wei Luen James Yip
|To handle this issue, we propose a model based on the Gated Recurrent Unit by decaying the effect of previous records using fine-grained feature-level time span information, and learn the decaying parameters for different features to take into account their different behaviours like decaying speeds under irregularity.
|Health Forum Thread Recommendation Using an Interest Aware Topic Model
|Kishaloy Halder, Min-Yen Kan, Kazunari Sugiyama
|We introduce a general, interest-aware topic model (IATM), in which known higher-level interests on topics expressed by each user can be modeled.
|HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks
|Liangzhe Chen, Xinfeng Xu, Sangkeun Lee, Sisi Duan, Alfonso G. Tarditi, Supriya Chinthavali, B. Aditya Prakash
|In this paper, we study this problem using a heterogeneous network viewpoint.
|SOPER: Discovering the Influence of Fashion and the Many Faces of User from Session Logs using Stick Breaking Process
|Lucky Dhakad, Mrinal Das, Chiranjib Bhattacharyya, Samik Datta, Mihir Kale, Vivek Mehta
|SOPER: Discovering the Influence of Fashion and the Many Faces of User from Session Logs using Stick Breaking Process
|Semi-Supervised Event-related Tweet Identification with Dynamic Keyword Generation
|Xin Zheng, Aixin Sun, Sibo Wang, Jialong Han
|In this paper, we propose a semi-supervised method to obtain high quality event-related tweets from Twitter stream, in terms of precision and recall.
|Distant Meta-Path Similarities for Text-Based Heterogeneous Information Networks
|Chenguang Wang, Yangqiu Song, Haoran Li, Yizhou Sun, Ming Zhang, Jiawei Han
|In this paper, we propose the distant meta-path similarity that is able to capture HIN semantics between two distant (isolated) entities to provide more meaningful entity proximity.
|Unsupervised Feature Selection with Joint Clustering Analysis
|Shuai An, Jun Wang, Jinmao Wei, Zhenglu Yang
|To address the problems, we propose a novel unsupervised approach that integrates sparse feature selection and robust joint clustering analysis.
|Multi-Label Feature Selection using Correlation Information
|Ali Braytee, Wei Liu, Daniel R. Catchpoole, Paul J. Kennedy
|In this paper, we propose a CMFS (Correlated- and Multi-label Feature Selection method), based on non-negative matrix factorization (NMF) for simultaneously performing feature selection and addressing the aforementioned challenges.
|Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation
|Yiyang Li, Guanyu Tao, Weinan Zhang, Yong Yu, Jun Wang
|In this paper, we consider to transfer knowledge from a larger text corpus.
|NeuPL: Attention-based Semantic Matching and Pair-Linking for Entity Disambiguation
|Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, Chenliang Li
|In this paper, we propose a deep neural network model to effectively measure the semantic matching between mention’s context and target entity.
|Relaxing Graph Pattern Matching With Explanations
|Jia Li, Yang Cao, Shuai Ma
|These together give us a framework for enriching the results of graph pattern matching.
|Active Network Alignment: A Matching-Based Approach
|Eric Malmi, Aristides Gionis, Evimaria Terzi
|This paper introduces two novel relative-query strategies, TopMatchings and GibbsMatchings, which can be applied on top of any network alignment method that constructs and solves a bipartite matching problem.
|Discovering Graph Temporal Association Rules
|Mohammad Hossein Namaki, Yinghui Wu, Qi Song, Peng Lin, Tingjian Ge
|This paper proposes graph temporal association rules (GTAR).
|Minimizing Tension in Teams
|Behzad Golshan, Evimaria Terzi
|The question we consider in this paper is the following: "can this tension be reduced by providing incentives to individuals to change their work habits?"
|Interactive Spatial Keyword Querying with Semantics
|Jiabao Sun, Jiajie Xu, Kai Zheng, Chengfei Liu
|To overcome this flaw, this paper investigates the interactive spatial keyword querying with semantics.
|From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
|Viet Ha-Thuc, Yan Yan, Xianren Wu, Vijay Dialani, Abhishek Gupta, Shakti Sinha
|This paper describes our approach to solving these challenges.
|Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
|Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca, Pascal Fleury
|In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion.
|Deep Context Modeling for Web Query Entity Disambiguation
|Zhen Liao, Xinying Song, Yelong Shen, Saekoo Lee, Jianfeng Gao, Ciya Liao
|In this paper, we presented a new study for Web query entity disambiguation (QED), which is the task of disambiguating different candidate entities in a knowledge base given their mentions in a query.
|An Attention-based Collaboration Framework for Multi-View Network Representation Learning
|Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han
|We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations.
|Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations
|Zhen Tan, Xiang Zhao, Wei Wang
|In this paper, we propose a novel knowledge graph embedding model, CombinE.
|Learning Edge Representations via Low-Rank Asymmetric Projections
|Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou
|We propose a new method for embedding graphs while preserving directed edge information.
|HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
|Tao-yang Fu, Wang-Chien Lee, Zhen Lei
|In this paper, we propose a novel representation learning framework, namely HIN2Vec, for heterogeneous information networks (HINs).
|Core Decomposition and Densest Subgraph in Multilayer Networks
|Edoardo Galimberti, Francesco Bonchi, Francesco Gullo
|We introduce a definition of multilayer densest subgraph that trades-off between high density and number of layers in which the high density holds, and show how multilayer core decomposition can be exploited to approximate this problem with quality guarantees.
|Fully Dynamic Algorithm for Top-
|Muhammad Anis Uddin Nasir, Aristides Gionis, Gianmarco De Francisci Morales, Sarunas Girdzijauskas
|In this paper, we study the top-k densest-subgraph problem in the sliding-window model and propose an efficient fully-dynamic algorithm.
|Minimizing Dependence between Graphs
|Yu Rong, Hong Cheng
|We propose two algorithms to solve GDM.
|Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results
|Mohamed Ghalwash, Ying Li, Ping Zhang, Jianying Hu
|We propose a method that leverages drug information to find a meaningful list of drugs that have an effect on the laboratory result.
|Efficient Discovery of Ontology Functional Dependencies
|Sridevi Baskaran, Alexander Keller, Fei Chiang, Lukasz Golab, Jaroslaw Szlichta
|Our technical contributions are twofold: 1) theoretical foundations for OFDs, including a set of sound and complete axioms and a linear-time inference procedure, and 2) an algorithm for discovering OFDs (exact ones and ones that hold with some exceptions) from data that uses the axioms to prune the exponential search space in the number of attributes.
|Automatic Navbox Generation by Interpretable Clustering over Linked Entities
|Chenhao Xie, Lihan Chen, Jiaqing Liang, Kezun Zhang, Yanghua Xiao, Hanghang Tong, Haixun Wang, Wei Wang
|In this paper, we target on the automatic generation of Navbox for Wikipedia articles.
|A Two-Stage Framework for Computing Entity Relatedness in Wikipedia
|Marco Ponza, Paolo Ferragina, Soumen Chakrabarti
|Introducing a new dataset with human judgments of entity relatedness, we present a thorough study of all entity relatedness measures in recent literature based on Wikipedia as the knowledge graph.
|Incorporating the Latent Link Categories in Relational Topic Modeling
|Yuan He, Cheng Wang, Changjun Jiang
|In this paper, we introduce a latent correlation factor to categorize the links into several categories, and each category corresponds to a unique kind of association.
|Tone Analyzer for Online Customer Service: An Unsupervised Model with Interfered Training
|Peifeng Yin, Zhe Liu, Anbang Xu, Taiga Nakamura
|In this work, by collecting and labeling online conversations of customer service on Twitter, we identify 8 new metrics, named as tones, to describe emotional information.
|Nationality Classification Using Name Embeddings
|Junting Ye, Shuchu Han, Yifan Hu, Baris Coskun, Meizhu Liu, Hong Qin, Steven Skiena
|We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classifiers and other systems.
|Emotions in Social Networks: Distributions, Patterns, and Models
|Shengmin Jin, Reza Zafarani
|Based on our observations, we propose the Emotional-Tie model — a network model that can simulate the formation of friendships based on emotions.
|Hike: A Hybrid Human-Machine Method for Entity Alignment in Large-Scale Knowledge Bases
|Yan Zhuang, Guoliang Li, Zhuojian Zhong, Jianhua Feng
|To achieve this goal, in this paper we propose a novel hybrid human-machine framework for large-scale KB integration.
|Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems
|Qingyun Wu, Hongning Wang, Liangjie Hong, Yue Shi
|In this work, we propose to improve long-term user engagement in a recommender system from the perspective of sequential decision optimization, where users’ click and return behaviors are directly modeled for online optimization.
|Predicting Startup Crowdfunding Success through Longitudinal Social Engagement Analysis
|Qizhen Zhang, Tengyuan Ye, Meryem Essaidi, Shivani Agarwal, Vincent Liu, Boon Thau Loo
|In this paper, we perform a longitudinal data collection and analysis of AngelList – a popular crowdfunding social platform for connecting investors and entrepreneurs.
|Optimizing Email Volume For Sitewide Engagement
|Rupesh Gupta, Guanfeng Liang, Romer Rosales
|In this paper we focus on the problem of optimizing email volume for maximizing sitewide engagement of an online social networking service.
|Understanding Engagement through Search Behaviour
|Mengdie Zhuang, Gianluca Demartini, Elaine G. Toms
|In this paper, we investigate the potential to predict how users perceive engagement with search by modelling behavioural signals from log files using supervised learning methods.
|Citation Metadata Extraction via Deep Neural Network-based Segment Sequence Labeling
|Dong An, Liangcai Gao, Zhuoren Jiang, Runtao Liu, Zhi Tang
|In this paper, we propose a sequence labeling model for citation metadata extraction, called segment sequence labeling.
|A Novel Approach for Efficient Computation of Community Aware Ridesharing Groups
|Samiul Anwar, Shuha Nabila, Tanzima Hashem
|We propose a novel way to form ridesharing groups that reveals user social data in community levels, and ensures that a group member shares at least k common communities with at least other m members in the ridesharing group, where k and m are personalized parameters of every group member.
|Extracting Entities of Interest from Comparative Product Reviews
|Jatin Arora, Sumit Agrawal, Pawan Goyal, Sayan Pathak
|This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites.
|A Neural Collaborative Filtering Model with Interaction-based Neighborhood
|Ting Bai, Ji-Rong Wen, Jun Zhang, Wayne Xin Zhao
|Based on this consideration, we propose a novel Neighborhood-based Neural Collaborative Filtering model (NNCF).
|Profiling DRDoS Attacks with Data Analytics Pipeline
|Laure Berti-Equille, Yury Zhauniarovich
|In this paper, we propose a first analytic pipeline that enables us to cluster and characterize attack campaigns into several main profiles that exhibit similarities.
|A Compare-Aggregate Model with Dynamic-Clip Attention for Answer Selection
|Weijie Bian, Si Li, Zhao Yang, Guang Chen, Zhiqing Lin
|In this paper, unlike previous Compare-Aggregate models which utilize the traditional attention mechanism to generate corresponding word-level vector before comparison, we propose a novel attention mechanism named Dynamic-Clip Attention which is directly integrated into the Compare-Aggregate framework.
|Learning Biological Sequence Types Using the Literature
|Mohamed Reda Bouadjenek, Karin Verspoor, Justin Zobel
|To address this problem of automatic sequence type classification, we propose the use of literature associated to sequence records as an external source of knowledge that can be leveraged for the classification task.
|Detecting Social Bots by Jointly Modeling Deep Behavior and Content Information
|Chiyu Cai, Linjing Li, Daniel Zeng
|The proposed model learns the representation of social behavior by encoding both endogenous and exogenous factors which affect user behavior.
|PMS: an Effective Approximation Approach for Distributed Large-scale Graph Data Processing and Mining
|Yingjie Cao, Yangyang Zhang, Jianxin Li
|In this paper, we propose an effective priority-based message sampling (PMS ) approach to further improve the performance of distributed graph processing at the cost of some accuracy loss.
|Language Modeling by Clustering with Word Embeddings for Text Readability Assessment
|Miriam Cha, Youngjune Gwon, H. T. Kung
|We present a clustering-based language model using word embeddings for text readability prediction.
|Compact Multiple-Instance Learning
|Jing Chai, Weiwei Liu, Ivor W. Tsang, Xiaobo Shen
|Two related issues might affect the performance of MIL algorithms: how to cope with label ambiguities and how to deal with non-discriminative components, and we propose COmpact MultiPle-Instance LEarning (COMPILE) to consider them simultaneously.
|Text Embedding for Sub-Entity Ranking from User Reviews
|Chih-Yu Chao, Yi-Fan Chu, Hsiu-Wei Yang, Chuan-Ju Wang, Ming-Feng Tsai
|To deal with such analysis, we propose a text embedding framework for ranking sub-entities from user reviews of a given super-entity.
|Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining
|Elaheh Alipour Chavary, Sarah M. Erfani, Christopher Leckie
|In this paper, we focus on finding important differences between network traffic datasets, and preparing a summarized and interpretable report for security managers.
|Modeling Opinion Influence with User Dual Identity
|Chengyao Chen, Zhitao Wang, Wenjie Li
|In this work, we explore users’ dual identities, including both personal identities and social identities to build a more comprehensive opinion influence model for a better understanding of opinion behaviors.
|An Empirical Analysis of Pruning Techniques: Performance, Retrievability and Bias
|Ruey-Cheng Chen, Leif Azzopardi, Falk Scholer
|In this paper, we investigate how the retrieval bias of a system changes as the inverted index is optimized for efficiency through static index pruning.
|Text Coherence Analysis Based on Deep Neural Network
|Baiyun Cui, Yingming Li, Yaqing Zhang, Zhongfei Zhang
|In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence.
|Unsupervised Matrix-valued Kernel Learning For One Class Classification
|Shaobo Dang, Xiongcai Cai, Yang Wang, Jianjia Zhang, Fang Chen
|This paper is concerned with the one class classification(OCC) problem.
|Analysis of Telegram, An Instant Messaging Service
|Arash Dargahi Nobari, Negar Reshadatmand, Mahmood Neshati
|In this paper, we developed a crawler to gather its public data.
|Estimating Event Focus Time Using Neural Word Embeddings
|Supratim Das, Arunav Mishra, Klaus Berberich, Vinay Setty
|We propose several estimators that leverage distributional event and time representations learned from large external document collections by adapting the word2vec paradigm.
|Personalized Image Aesthetics Assessment
|Xiang Deng, Chaoran Cui, Huidi Fang, Xiushan Nie, Yilong Yin
|In this paper, we propose to model user aesthetic perceptions using a set of exemplar images from social media platforms, and realize personalized aesthetics assessment by transferring this knowledge to adapt the results of the trained generic model.
|Efficient Fault-Tolerant Group Recommendation Using alpha-beta-core
|Danhao Ding, Hui Li, Zhipeng Huang, Nikos Mamoulis
|To address this issue, we model the fault-tolerant subspace clustering problem as a search problem on graphs and present an algorithm, GraphRec, based on the concept of α-ß-core.
|On Discovering the Number of Document Topics via Conceptual Latent Space
|Nghia Duong-Trung, Lars Schmidt-Thieme
|In this paper, we study the concept of conceptual stability via nonnegative matrix factorization.
|Chinese Named Entity Recognition with Character-Word Mixed Embedding
|Shijia E, Yang Xiang
|To solve this issue, we propose a Chinese NER method based on Character-Word Mixed Embedding (CWME), and the method is in accord with the pipeline of Chinese natural language processing.
|An Empirical Study of Embedding Features in Learning to Rank
|Faezeh Ensan, Ebrahim Bagheri, Amal Zouaq, Alexandre Kouznetsov
|We have extensively introduced and investigated the effectiveness of features learnt based on word and document embeddings to represent both queries and documents.
|Privacy of Hidden Profiles: Utility-Preserving Profile Removal in Online Forums
|Sedigheh Eslami, Asia J. Biega, Rishiraj Saha Roy, Gerhard Weikum
|In this work, we investigate an alternative solution to standard profile removal, where posts of different users are split and merged into synthetic mediator profiles.
|QoS-Aware Scheduling of Heterogeneous Servers for Inference in Deep Neural Networks
|Zhou Fang, Tong Yu, Ole J. Mengshoel, Rajesh K. Gupta
|This paper represents the QoS metric as a utility function of response delay and inference accuracy.
|Geographic and Temporal Trends in Fake News Consumption During the 2016 US Presidential Election
|Adam Fourney, Miklos Z. Racz, Gireeja Ranade, Markus Mobius, Eric Horvitz
|We propose a simple model based on homophily in social networks to explain the linear association.
|Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks
|Felan Carlo C. Garcia, Erees Queen B. Macabebe
|In this paper we present a method to provide appliance energy usage feedback from smart meters using energy disaggregation.
|Tracking the Impact of Fact Deletions on Knowledge Graph Queries using Provenance Polynomials
|Garima Gaur, Srikanta J. Bedathur, Arnab Bhattacharya
|We propose a framework based on provenance polynomials to track the impact of knowledge graph changes on arbitrary SPARQL query results.
|An Euclidean Distance based on the Weighted Self-information Related Data Transformation for Nominal Data Clustering
|Lei Gu, Liying Zhang, Yang Zhao
|This paper mainly aims to make the Euclidean distance measure appropriate to nominal data clustering, and the core idea is to transform each nominal attribute value into numerical.
|Interest Diffusion in Heterogeneous Information Network for Personalized Item Ranking
|Mukul Gupta, Pradeep Kumar, Rajhans Mishra
|In this paper, we deal with the problem of the sparseness of data and accuracy of recommendations.
|Source Retrieval for Web-Scale Text Reuse Detection
|Matthias Hagen, Martin Potthast, Payam Adineh, Ehsan Fatehifar, Benno Stein
|We propose a new approach that reaches a recall of~0.89—a performance gain of~51%.
|Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care
|Casper Hansen, Christian Hansen, Stephen Alstrup, Christina Lioma
|We present an ensemble learning method that predicts large increases in the hours of home care received by citizens.
|Fast K-means for Large Scale Clustering
|Qinghao Hu, Jiaxiang Wu, Lu Bai, Yifan Zhang, Jian Cheng
|In this paper, we propose a fast k-means algorithm named multi-stage k-means (MKM) which uses a multi-stage filtering approach.
|Graph Ladder Networks for Network Classification
|Ruiqi Hu, Shirui Pan, Jing Jiang, Guodong Long
|In this paper, we propose an effective deep learning model, Graph Ladder Networks (GLN), for node classification in networks.
|A Communication Efficient Parallel DBSCAN Algorithm based on Parameter Server
|Xu Hu, Jun Huang, Minghui Qiu
|In this paper, we propose PS-DBSCAN, a parallel DBSCAN algorithm that combines the disjoint-set data structure and Parameter Server framework, to minimize communication cost.
|KIEM: A Knowledge Graph based Method to Identify Entity Morphs
|Longtao Huang, Lin Zhao, Shangwen Lv, Fangzhou Lu, Yue Zhai, Songlin Hu
|In this paper, we introduce a novel method based on knowledge graph, which takes advantage of both knowledge reasoning and statistic learning.
|Ontology-based Graph Visualization for Summarized View
|Xin Huang, Byron Choi, Jianliang Xu, William K. Cheung, Yanchun Zhang, Jiming Liu
|In this paper, we study the problem of selecting a diverse set of k elements to summarize an input dataset with hierarchical terminologies, and visualize the summary in an ontology structure.
|An Ad CTR Prediction Method Based on Feature Learning of Deep and Shallow Layers
|Zai Huang, Zhen Pan, Qi Liu, Bai Long, Haiping Ma, Enhong Chen
|To address the shortcomings above, in this paper, we propose a novel hybrid method based on feature learning of both Deep and Shallow Layers (DSL).
|A Framework for Estimating Execution Times of IO Traces on SSDs
|Yoonsuk Kang, Yong-Yeon Jo, Jaehyuk Cha, Wan D. Bae, Sang-Wook Kim
|In this paper, we propose a framework of estimating the execution time of an application IO trace (i.e., a query IO trace) on a target SSD without its real execution.
|Ranking Rich Mobile Verticals based on Clicks and Abandonment
|Mami Kawasaki, Inho Kang, Tetsuya Sakai
|In order to provide the right card types to the user for a given query, we propose a graph-based approach which extends a click-based automatic relevance estimation algorithm of Agrawal et al., by incorporating an abandonment-based preference rule. Using a real mobile query log from a commercial search engine, we constructed a data set containing 2,472 pairwise card type preferences covering 992 distinct queries, by hiring three independent assessors.
|Semantic Rules for Machine Diagnostics: Execution and Management
|Evgeny Kharlamov, Ognjen Savkoviý, Guohui Xiao, Rafael Penaloza, Gulnar Mehdi, Mikhail Roshchin, Ian Horrocks
|In this paper we present how semantic technologies can enhance diagnostics.
|Machine Learning based Performance Modeling of Flash SSDs
|Jaehyung Kim, Jinuk Park, Sanghyun Park
|In this paper, we examine the effectiveness of applying classification method using machine learning techniques to the I/O saturation estimation by using Linux kernel I/O statistics instead of the utilization measure that is currently used for HDDs.
|A Robust Named-Entity Recognition System Using Syllable Bigram Embedding with Eojeol Prefix Information
|Sunjae Kwon, Youngjoong Ko, Jungyun Seo
|This paper proposes a novel syllable-level NER system, which does not require a morphological analysis and can achieve a similar or better performance compared with the morphological-level NER systems.
|IDAE: Imputation-boosted Denoising Autoencoder for Collaborative Filtering
|Jae-woong Lee, Jongwuk Lee
|In this paper, we propose a new CF model, namely the imputation-boosted denoising autoencoder (IDAE), for top-N recommendation.
|Computing Betweenness Centrality in B-hypergraphs
|Kwang Hee Lee, Myoung Ho Kim
|In this paper every source node of a hyperedge in the shortest path p in a B-hypergraph is considered a participant of p.
|Structural-fitting Word Vectors to Linguistic Ontology for Semantic Relatedness Measurement
|Yang-Yin Lee, Ting-Yu Yen, Hen-Hsen Huang, Hsin-Hsi Chen
|In this research, we propose a novel structural-fitting method that utilizes the linguistic ontology into vector space representations.
|Alternating Pointwise-Pairwise Learning for Personalized Item Ranking
|Yu Lei, Wenjie Li, Ziyu Lu, Miao Zhao
|This paper proposes a novel joint learning method named alternating pointwise-pairwise learning (APPL) to improve ranking performance.
|Deep Multi-Similarity Hashing for Multi-label Image Retrieval
|Tong Li, Sheng Gao, Yajing Xu
|In this paper, we proposed a framework named Deep Multi-Similarity Hashing (DMSH) method to learn semantic binary representations for multi-label image retrieval task.
|Learning Graph-based Embedding For Time-Aware Product Recommendation
|Yuqi Li, Weizheng Chen, Hongfei Yan
|In this paper, we propose a novel Product Graph Embedding (PGE) model to investigate time-aware product recommendation by leveraging the network representation learning technique.
|An Enhanced Topic Modeling Approach to Multiple Stance Identification
|Junjie Lin, Wenji Mao, Yuhao Zhang
|In this paper, we address the problem of recognizing distinct standpoints implied in textual data.
|TICC: Transparent Inter-Column Compression for Column-Oriented Database Systems
|Hao Liu, Yudian Ji, Jiang Xiao, Haoyu Tan, Qiong Luo, Lionel M. Ni
|In this paper, we present TICC, an automatic data compression component that can transparently eliminate data redundancies across columns in column-oriented database systems.
|Exploiting User Consuming Behavior for Effective Item Tagging
|Shen Liu, Hongyan Liu
|In this paper, we propose to leverage such information and introduce a probabilistic model called joint-tagging LDA to improve tagging accuracy.
|SEQ: Example-based Query for Spatial Objects
|Siqiang Luo, Jiafeng Hu, Reynold Cheng, Jing Yan, Ben Kao
|In this paper, we propose the Spatial Exemplar Query (SEQ), which allows the user to input a result example over an interface inside the map service.
|Truth Discovery by Claim and Source Embedding
|Shanshan Lyu, Wentao Ouyang, Huawei Shen, Xueqi Cheng
|Given these limitations, we propose a new, unsupervised model for truth discovery in this paper.
|Automatic Catchphrase Identification from Legal Court Case Documents
|Arpan Mandal, Kripabandhu Ghosh, Arindam Pal, Saptarshi Ghosh
|In this work, we propose an unsupervised approach for extraction and ranking of catchphrases from court case documents, by focusing on noun phrases.
|Learning Temporal Ambiguity in Web Search Queries
|Behrooz Mansouri, Mohammad Sadegh Zahedi, Maseud Rahgozar, Farhad Oroumchian, Ricardo Campos
|In this paper, we propose an approach to classify web queries into four different categories considering their temporal ambiguity.
|Online Expectation-Maximization for Click Models
|Ilya Markov, Alexey Borisov, Maarten de Rijke
|To deal with outdated click information, we propose a variant of online EM called EM with Forgetting, which surpasses the performance of complete retraining while being as efficient as Online EM.
|Task Embeddings: Learning Query Embeddings using Task Context
|Rishabh Mehrotra, Emine Yilmaz
|In this work, we hypothesize that task information provides better context for IR systems to learn from.
|Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection
|Zhao Meng, Lili Mou, Zhi Jin
|We formulate text-based SCD as a matching problem of utterances before and after a certain decision point; we propose a hierarchical recurrent neural network (RNN) with static sentence-level attention.
|Predicting Short-Term Public Transport Demand via Inhomogeneous Poisson Processes
|Aditya Krishna Menon, Young Lee
|In this paper, we show how such short term demand can be accurately modelled with an inhomogeneous Poisson process, using a neural network as the underlying intensity.
|Analyzing Mathematical Content to Detect Academic Plagiarism
|Norman Meuschke, Moritz Schubotz, Felix Hamborg, Tomas Skopal, Bela Gipp
|This paper presents, to our knowledge, the first study on analyzing mathematical expressions to detect academic plagiarism. To facilitate future research on math-based plagiarism detection, we make our source code and data available.
|Learning Entity Type Embeddings for Knowledge Graph Completion
|Changsung Moon, Paul Jones, Nagiza F. Samatova
|Inspired by recent work to build a contextual KG embedding model, we propose a novel approach to address the entity type prediction problem.
|Identifying Top-K Influential Nodes in Networks
|Sara Mumtaz, Xiaoyang Wang
|With an attempt to deal with these challenges, our paper presents an approximate algorithm for BC maximization problem, which tries to find a set of nodes with largest BC.
|Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation
|Mir Tafseer Nayeem, Yllias Chali
|This paper presents a first attempt towards finding an abstractive compression generation system for a set of related sentences which jointly models sentence fusion and paraphrasing using continuous vector representations.
|J-REED: Joint Relation Extraction and Entity Disambiguation
|Dat Ba Nguyen, Martin Theobald, Gerhard Weikum
|This paper presents J-REED: a joint approach for entity disambiguation and relation extraction that is based on probabilistic graphical models.
|Collaborative Topic Regression with Denoising AutoEncoder for Content and Community Co-Representation
|Trong T. Nguyen, Hady W. Lauw
|We seek to integrate both types of information, in addition to the adoption information, within a single integrated model.
|Accurate Sentence Matching with Hybrid Siamese Networks
|Massimo Nicosia, Alessandro Moschitti
|In this paper, we learn sentence representations by means of a siamese network, which: (i) uses encoders that share parameters; and (ii) enables the comparison between two sentences in terms of their euclidean distance, by minimizing a contrastive loss.
|Collaborative Sequence Prediction for Sequential Recommender
|Shuzi Niu, Rongzhi Zhang
|We propose to formulate the sequential recommendation problem as collaborative sequence prediction problem to take the dependency of users’ sequences into account.
|Boolean Matrix Decomposition by Formal Concept Sampling
|Petr Osicka, Martin Trnecka
|We describe and experimentally evaluate a probabilistic algorithm for Boolean matrix decomposition problem.
|Enhancing Knowledge Graph Completion By Embedding Correlations
|Soumajit Pal, Jacopo Urbani
|Statistical relational learning methods can detect missing links by "embedding" the nodes and relations into latent feature tensors.
|Robust Heterogeneous Discriminative Analysis for Single Sample Per Person Face Recognition
|Meng Pang, Yiu-ming Cheung, Binghui Wang, Risheng Liu
|In this work, we propose a new patch-based method, namely Robust Heterogeneous Discriminative Analysis (RHDA), to tackle FR with SSPP.
|Deep Neural Networks for News Recommendations
|Keunchan Park, Jisoo Lee, Jaeho Choi
|In this paper, we introduce deep neural network models to overcome these challenges.
|TATHYA: A Multi-Classifier System for Detecting Check-Worthy Statements in Political Debates
|Ayush Patwari, Dan Goldwasser, Saurabh Bagchi
|We introduce a dataset of political debates from the 2016 US Presidential election campaign annotated using all major fact-checking media outlets and show that there is a need to model conversation context, debate dynamics and implicit world knowledge.
|A Collaborative Ranking Model for Cross-Domain Recommendations
|Dimitrios Rafailidis, Fabio Crestani
|In this study, we propose a collaborative ranking model to generate cross-domain recommendations.
|Combining Local and Global Word Embeddings for Microblog Stemming
|Anurag Roy, Trishnendu Ghorai, Kripabandhu Ghosh, Saptarshi Ghosh
|We propose an unsupervised, context-specific stemming algorithm for microblogs, based on both local and global word embeddings, which is capable of handling the informal, noisy vocabulary of microblogs.
|An Improved Test Collection and Baselines for Bibliographic Citation Recommendation
|In this paper, we propose a way to modify this test collection to address these limitations.
|A Way to Boost Semi-NMF for Document Clustering
|Aghiles Salah, Melissa Ailem, Mohamed Nadif
|Inspired by the recent success of neural word embedding models, e.g., word2vec, in learning high quality real valued vector representations of words, we propose to integrate a word embedding model into Semi-NMF.
|Recipe Popularity Prediction with Deep Visual-Semantic Fusion
|Satoshi Sanjo, Marie Katsurai
|This paper presents a novel approach to predicting recipe popularity using deep visual-semantic fusion.
|Revealing the Hidden Links in Content Networks: An Application to Event Discovery
|Antonia Saravanou, Ioannis Katakis, George Valkanas, Vana Kalogeraki, Dimitrios Gunopulos
|In this paper, we focus on how Content Networks can help us identify events effectively.
|When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes
|Arun V. Sathanur, Sutanay Choudhury, Cliff Joslyn, Sumit Purohit
|In this work we tackle the problem of capturing the statistical dependence of the edge connectivity on the vertex labels and using the same distribution to regenerate property graphs of the same or expanded size in a scalable manner.
|Integrating the Framing of Clinical Questions via PICO into the Retrieval of Medical Literature for Systematic Reviews
|Harrisen Scells, Guido Zuccon, Bevan Koopman, Anthony Deacon, Leif Azzopardi, Shlomo Geva
|The PICO framework is used extensively in the compilation of systematic reviews as the means of framing research questions.
|pm-SCAN: an I/O Efficient Structural Clustering Algorithm for Large-scale Graphs
|Jung Hyuk Seo, Myoung Ho Kim
|We propose an I/O-efficient algorithm for structural clustering, pm-SCAN.
|Knowledge Graph Embedding with Triple Context
|Jun Shi, Huan Gao, Guilin Qi, Zhangquan Zhou
|In this paper, we take advantages of structures in knowledge graphs, especially local structures around a triple, which we refer to as triple context.
|Hybrid MemNet for Extractive Summarization
|Abhishek Kumar Singh, Manish Gupta, Vasudeva Varma
|To this end, we present a fully data-driven end-to-end deep network which we call as Hybrid MemNet for single document summarization task.
|Denoising Clinical Notes for Medical Literature Retrieval with Convolutional Neural Model
|Luca Soldaini, Andrew Yates, Nazli Goharian
|In this work, we present a convolutional neural model aimed at improving clinical notes representation, making them suitable for document retrieval.
|SIMD-Based Multiple Sets Intersection with Dual-Scale Search Algorithm
|Xingshen Song, Yuexiang Yang, Xiaoyong Li
|We present a flexible search algorithm which balances non-SIMD and SIMD comparisons in order to provide efficient and effective intersection.
|Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval
|Avikalp Srivastava, Madhav Datt
|We propose a novel unsupervised model for semantic similarity based content retrieval, where we construct semantic flow graphs for each query, and introduce the concept of "soft seeding" in graph based semi-supervised learning (SSL) to convert this into an unsupervised model.
|How Safe is Your (Taxi) Driver?
|In this paper we discuss a methodology for studying driver risk assessment using a public dataset of 173M taxi rides in NYC with over 40K drivers.
|Sentence Retrieval with Sentiment-specific Topical Anchoring for Review Summarization
|Jiaxing Tan, Alexander Kotov, Rojiar Pir Mohammadiani, Yumei Huo
|We propose Topic Anchoring-based Review Summarization (TARS), a two-step extractive summarization method, which creates review summaries from the sentences that represent the most important aspects of a review.
|Visualizing Deep Neural Networks with Interaction of Super-pixels
|Shixin Tian, Ying Cai
|In the existing works, these units are largely considered independently, thus limiting the performance of visualization.
|Collecting Non-Geotagged Local Tweets via Bandit Algorithms
|Saki Ueda, Yuto Yamaguchi, Hiroyuki Kitagawa
|In this paper, we propose a framework that incrementally finds such users and continuously collects tweets from them.
|A Temporal Attentional Model for Rumor Stance Classification
|Amir Pouran Ben Veyseh, Javid Ebrahimi, Dejing Dou, Daniel Lowd
|In this work, we analyze Twitter users’ stance toward a rumorous tweet, in which users could support, deny, query, or comment upon the rumor.
|Improving the Gain of Visual Perceptual Behaviour on Topic Modeling for Text Recommendation
|Cheng Wang, Yujuan Fang, Zheng Tan, Yuan He
|In this paper, we mainly aim at improving the gain of visual perceptual behaviour for text recommendation, by integrating the objective contents with subjective visual perceptual behaviours.
|Semantic Annotation for Places in LBSN through Graph Embedding
|Yan Wang, Zongxu Qin, Jun Pang, Yang Zhang, Jin Xin
|Our underlying idea is that a place can be considered as a representative of all its visitors.
|A Study of Feature Construction for Text-based Forecasting of Time Series Variables
|Yiren Wang, Dominic Seyler, Shubhra Kanti Karmaker Santu, ChengXiang Zhai
|In this paper, we study how to construct effective additional features based on related text data for time series forecasting.
|Using Knowledge Graphs to Explain Entity Co-occurrence in Twitter
|Yiwei Wang, Mark James Carman, Yuan-Fang Li
|In this paper we investigate whether these two sources of information can be used to complement and explain one another.
|Integrating Side Information for Boosting Machine Comprehension
|Yutong Wang, Yixin Xu, Min Yang, Zhou Zhao, Jun Xiao, Yueting Zhuang
|In this paper, we consider integrating side information to improve machine comprehension on answering cloze-style questions more precisely.
|Unsupervised Feature Selection with Heterogeneous Side Information
|Xiaokai Wei, Bokai Cao, Philip S. Yu
|In this paper, we propose a new feature selection method, SideFS, to exploit such rich side information.
|An Empirical Study of Community Overlap: Ground-truth, Algorithmic Solutions, and Implications
|Joyce Jiyoung Whang
|In this paper, we investigate the properties of the nodes and the edges placed within the overlapped regions between the communities using the ground-truth communities as well as algorithmic communities derived from the state-of-the-art overlapping community detection methods.
|Non-Exhaustive, Overlapping Co-Clustering
|Joyce Jiyoung Whang, Inderjit S. Dhillon
|To solve this problem, we propose an intuitive objective function, and develop an efficient iterative algorithm which we call the NEO-CC algorithm.
|Simulating Zero-Resource Spoken Term Discovery
|Jerome White, Douglas W. Oard
|This paper describes a text-based simulation of a zero-resource spoken term discovery system that allows any information retrieval test collection to be used as a basis for early development of information retrieval techniques.
|Algorithmic Bias: Do Good Systems Make Relevant Documents More Retrievable?
|Colin Wilkie, Leif Azzopardi
|This work evaluates the varying degrees of bias present in the groups of relevant and non-relevant documents for topics.
|Session-aware Information Embedding for E-commerce Product Recommendation
|Chen Wu, Ming Yan
|In this paper, we propose a list-wise deep neural network based architecture to model the limited user behaviors within each session.
|Conflict of Interest Declaration and Detection System in Heterogeneous Networks
|Siyuan Wu, Leong Hou U, Sourav S. Bhowmick, Wolfgang Gatterbauer
|In this work, we study a graphical declaration system that visualizes the relationships of authors and reviewers based on a heterogeneous co-authorship network.
|Common-Specific Multimodal Learning for Deep Belief Network
|Changsheng Xiang, Xiaoming Jin
|This paper proposes the Common-Specific Multimodal Deep Belief Network (CSDBN) to solve the problem.
|JointSem: Combining Query Entity Linking and Entity based Document Ranking
|Chenyan Xiong, Zhengzhong Liu, Jamie Callan, Eduard Hovy
|This work presents JointSem, a joint semantic ranking system that combines query entity linking and entity-based document ranking.
|Learning to Rank with Query-level Semi-supervised Autoencoders
|Bo Xu, Hongfei Lin, Yuan Lin, Kan Xu
|To enrich the feature space for learning to rank, we introduce supervision into the loss functions of autoencoders.
|MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis
|Nan Xu, Wenji Mao
|In this paper, we propose a deep semantic network, namely MultiSentiNet, for multimodal sentiment analysis.
|Attentive Graph-based Recursive Neural Network for Collective Vertex Classification
|Qiongkai Xu, Qing Wang, Chenchen Xu, Lizhen Qu
|In this paper, we propose an Attentive Graph-based Recursive Neural Network (AGRNN), which exerts attention on neural network to make our model focus on vertices with more relevant semantic information.
|Bayesian Heteroscedastic Matrix Factorization for Conversion Rate Prediction
|We focus on matrix CVR predictions in this paper but the proposed BHMF can be naturally extended and applied to higher dimensional tensors.
|SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories
|Di Yao, Chao Zhang, Jianhui Huang, Jingping Bi
|We propose a method named semantics-enriched recurrent model (SERM).
|Low-Rank Matrix Completion over Finite Abelian Group Algebras for Context-Aware Recommendation
|Chia-An Yu, Tak-Shing Chan, Yi-Hsuan Yang
|In this paper, we address this by using matrices over finite abelian group algebra (AGA) to model context-aware interactions between users and items.
|Spectrum-based Deep Neural Networks for Fraud Detection
|Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu
|In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data.
|RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation
|Yu Zhang, Wei Wei, Binxuan Huang, Kathleen M. Carley, Yan Zhang
|In this paper, we aim to tackle these two problems.
|Missing Value Learning
|Zhi-Lin Zhao, Chang-Dong Wang, Kun-Yu Lin, Jian-Huang Lai
|In this paper, we focus on learning from the known values to learn missing value as close as possible to the true one.
|Local Ensemble across Multiple Sources for Collaborative Filtering
|Jing Zheng, Fuzhen Zhuang, Chuan Shi
|In this paper, we propose a novel LO cal EN semble framework across multiple source domains for collaborative filtering (called LOEN for short), where weights of multiple sources for each missing rating in the target domain are determined according to their corresponding local structures.
|Cluster-level Emotion Pattern Matching for Cross-Domain Social Emotion Classification
|Endong Zhu, Yanghui Rao, Haoran Xie, Yuwei Liu, Jian Yin, Fu Lee Wang
|This paper proposes a novel framework, which uses the emotion distribution of training documents at the cluster level, to alleviate the aforementioned issue.
|Knowledge-based Question Answering by Jointly Generating, Copying and Paraphrasing
|Shuguang Zhu, Xiang Cheng, Sen Su, Shuang Lang
|In this paper, we focus on simple questions, which ask about only a subject and relation in the knowledge base.
|PODIUM: Procuring Opinions from Diverse Users in a Multi-Dimensional World
|Yael Amsterdamer, Oded Goldreich
|We present PODIUM, a tool for opinion procurement that accounts for complex user profiles and enables customizable user selection.
|VizQ: A System for Scalable Processing of Visibility Queries in 3D Spatial Databases
|Arif Arman, Mohammed Eunus Ali, Farhana Murtaza Choudhury, Kaysar Abdullah
|In this demonstration, we present VizQ, an efficient, scalable, and interactive system to process and visualize a comprehensive collection of novel visibility queries in the presence of obstacles in 3D space.
|CoreDB: a Data Lake Service
|Amin Beheshti, Boualem Benatallah, Reza Nouri, Van Munin Chhieng, HuangTao Xiong, Xu Zhao
|To address the above mentioned challenges, we present CoreDB – an open source data lake service – which offers researchers and developers a single REST API to organize, index and query their data and metadata.
|SimMeme: Semantic-Based Meme Search
|Maya Ekron, Tova Milo, Brit Youngmann
|In this work, we focus on a particular class of images that convey semantic meaning beyond the visual appearance, and whose search presents particular challenges.
|SummIt: A Tool for Extractive Summarization, Discovery and Analysis
|Guy Feigenblat, Odellia Boni, Haggai Roitman, David Konopnicki
|We propose to demonstrate SummIt — a tool for extractive summarization, discovery and analysis.
|Rapid Analysis of Network Connectivity
|Scott Freitas, Hanghang Tong, Nan Cao, Yinglong Xia
|To facilitate this process we utilize: (1) multi-threaded algorithm variations, (2) network re-use for subsequent queries and (3) a novel algorithm, Key Neighboring Vertices (KNV), to reduce the network search space.
|HyPerInsight: Data Exploration Deep Inside HyPer
|Nina Hubig, Linnea Passing, Maximilian E. Schüle, Dimitri Vorona, Alfons Kemper, Thomas Neumann
|We propose to extend HyPer, a main memory database system to a uniform data agent platform following the one system fits all approach for solving a wide variety of data analysis problems.
|Interactive System for Reasoning about Document Age
|Adam Jatowt, Ricardo Campos
|In this paper, we demonstrate an interactive system for estimating the age of documents.
|SemFacet: Making Hard Faceted Search Easier
|Evgeny Kharlamov, Luca Giacomelli, Evgeny Sherkhonov, Bernardo Cuenca Grau, Egor V. Kostylev, Ian Horrocks
|In this demo we present (an extension of) our faceted search system SemFacet and focus on features that address the information overload: ranking, aggregation, and reachability.
|Metacrate: Organize and Analyze Millions of Data Profiles
|Sebastian Kruse, David Hahn, Marius Walter, Felix Naumann
|In particular, we (i) propose a logical and a physical data model to store all kinds of data profiles in a scalable fashion; (ii) describe an analytics layer to query, integrate, and analyze the profiles efficiently; and (iii) implement on top a library of established algorithms to serve use cases, such as schema discovery, database refactoring, and data cleaning.
|SemVis: Semantic Visualization for Interactive Topical Analysis
|Tuan M. V. Le, Hady W. Lauw
|Semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially.
|Exploring the Veracity of Online Claims with BackDrop
|Julien Leblay, Weiling Chen, Steven Lynden
|We present BackDrop, an application that enables annotating knowledge and ontologies found online to explore how the veracity of claims varies with context.
|AliMe Assist : An Intelligent Assistant for Creating an Innovative E-commerce Experience
|Feng-Lin Li, Minghui Qiu, Haiqing Chen, Xiongwei Wang, Xing Gao, Jun Huang, Juwei Ren, Zhongzhou Zhao, Weipeng Zhao, Lei Wang, Guwei Jin, Wei Chu
|In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.
|Public Transportation Mode Detection from Cellular Data
|Guanyao Li, Chun-Jie Chen, Sheng-Yun Huang, Ai-Jou Chou, Xiaochuan Gou, Wen-Chih Peng, Chih-Wei Yi
|In this paper, we refer to some external data sources (e.g., the bus routing networks) to identify transportation modes.
|Urbanity: A System for Interactive Exploration of Urban Dynamics from Streaming Human Sensing Data
|Mengxiong Liu, Zhengchao Liu, Chao Zhang, Keyang Zhang, Quan Yuan, Tim Hanratty, Jiawei Han
|We present Urbanity, a novel system that leverages geo-tagged social media streams for modeling urban dynamics.
|SemDia: Semantic Rule-Based Equipment Diagnostics Tool
|Gulnar Mehdi, Evgeny Kharlamov, Ognjen Savković, Guohui Xiao, Elem Güzel Kalayci, Sebastian Brandt, Ian Horrocks, Mikhail Roshchin, Thomas Runkler
|In this demo we present how semantic technologies can enhance diagnostics.
|TaCLe: Learning Constraints in Tabular Data
|Sergey Paramonov, Samuel Kolb, Tias Guns, Luc De Raedt
|To address this issue we have introduced the TaCLe system (Tabular Constraint Learner).
|An Interactive Framework for Video Surveillance Event Detection and Modeling
|Fabio Persia, Fabio Bettini, Sven Helmer
|We present a framework for high-level event detection in video streams based on a novel temporal extension of relational algebra.
|Storyfinder: Personalized Knowledge Base Construction and Management by Browsing the Web
|Steffen Remus, Manuel Kaufmann, Kathrin Ballweg, Tatiana von Landesberger, Chris Biemann
|This paper presents Storyfinder, an application which consists of a browser plugin and a web server backend with the goal to highlight and manage the information contained in web pages by combining techniques from natural language processing and visual analytics.
|IMaxer: A Unified System for Evaluating Influence Maximization in Location-based Social Networks
|Muhammad Aamir Saleem, Rohit Kumar, Toon Calders, Xike Xie, Torben Bach Pedersen
|In this demonstration, we present a unified system IMaxer that both provides a complete pipeline of state-of-the-art and novel models and algorithms for influence maximization (IM) as well as allows to evaluate and compare IM techniques for a particular scenario.
|StreamingCube: A Unified Framework for Stream Processing and OLAP Analysis
|Salman Ahmed Shaikh, Hiroyuki Kitagawa
|To this end, we present StreamingCube, a unified framework for data stream processing and its interactive OLAP analysis.
|Product Exploration based on Latent Visual Attributes
|Tomáš Skopal, Ladislav Peška, Gregor Kovalčík, Tomáš Grosup, Jakub Lokoč
|In this demo paper, we present a prototype web application of a product search engine of a fashion e-shop.
|Hierarchical Module Classification in Mixed-initiative Conversational Agent System
|Sia Xin Yun Suzanna, Li Lianjie Anthony
|We address these challenges with a mixed-initiative model that controls conversational logic through hierarchical classification.
|Blockchain-based Data Management and Analytics for Micro-insurance Applications
|Hoang Tam Vo, Lenin Mehedy, Mukesh Mohania, Ermyas Abebe
|In this paper, we demonstrate a blockchain-based solution for transparently managing and analyzing data in a pay-as-you-go car insurance application.
|CleanCloud: Cleaning Big Data on Cloud
|Hongzhi Wang, Xiaoou Ding, Xiangying Chen, Jianzhong Li, Hong Gao
|We describe CleanCloud, a system for cleaning big data based on Map-Reduce paradigm in cloud.
|Interactive Analytics System for Exploring Outliers
|Mingrui Wei, Lei Cao, Chris Cormier, Hui Zheng, Elke A. Rundensteiner
|We demonstrate ONION’s capabilities with urban planning applications use cases on the Open Street Maps dataset.
|Query and Animate Multi-attribute Trajectory Data
|Jianqiu Xu, Ralf Hartmut Güting
|In this demo, we provide the motivation scenario and introduce a system that is developed to integrate standard trajectories (a sequence of timestamped locations) and attributes into one unified framework.
|ClaimVerif: A Real-time Claim Verification System Using the Web and Fact Databases
|Shi Zhi, Yicheng Sun, Jiayi Liu, Chao Zhang, Jiawei Han
|We build ClaimVerif, a claim verification system that not only provides credibility assessment for any user-given query claim, but also rationales the assessment results with supporting evidences.
|POOLSIDE: An Online Probabilistic Knowledge Base for Shopping Decision Support
|Ping Zhong, Zhanhuai Li, Qun Chen, Yanyan Wang, Lianping Wang, Murtadha HM Ahmed, Fengfeng Fan
|We present POOLSIDE, an online PrObabilistic knOwLedge base for ShoppIng DEcision support, that provides with the on-target recommendation service based on explicit user requirement.
|Overview of the 4th HistoInformatics Workshop
|Mohammed Hasanuzzaman, Gaël Dias, Adam Jatowt, Marten Düring, Antal van den Bosch
|The HistoInformatics workshop series is focused on the challenges and opportunities of data-driven humanities and brings together scientists and scholars at the forefront of this emerging field, at the interface between History, Anthropology, Archaeology, Computer Science and associated disciplines as well as the cultural heritage sector.
|IDM 2017: Workshop on Interpretable Data Mining — Bridging the Gap between Shallow and Deep Models
|Xia Hu, Shuiwang Ji
|This workshop is about interpreting the prediction mechanisms or results of the complex computational models for data mining by taking advantage of simple models which are easier to understand.
|SMASC 2017: First International Workshop on Social Media Analytics for Smart Cities
|Manjira Sinha, Xiangnan He, Alessandro Bozzon, Sandya Mannarswamy, Pradeep Murukannaiah, Tridib Mukherjee
|The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications for smart cities.
|Additional Workshops Co-located with CIKM 2017
|Additional Workshops Co-located with CIKM 2017