Paper Digest: CIKM 2018 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 2018 Papers/Short Papers
|Multi-Source Pointer Network for Product Title Summarization
|Fei Sun, Peng Jiang, Hanxiao Sun, Changhua Pei, Wenwu Ou, Xiaobo Wang
|In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices.
|Exploring a High-quality Outlying Feature Value Set for Noise-Resilient Outlier Detection in Categorical Data
|Hongzuo Xu, Yongjun Wang, Li Cheng, Yijie Wang, Xingkong Ma
|This paper introduces a novel unsupervised framework termed OUVAS, and its parameter-free instantiation RHAC to explore a high-quality outlying value set for detecting outliers in noisy categorical data.
|Neural Relational Topic Models for Scientific Article Analysis
|Haoli Bai, Zhuangbin Chen, Michael R. Lyu, Irwin King, Zenglin Xu
|To this end, we propose a novel Bayesian deep generative model termed as Neural Relational Topic Model (NRTM), which is composed with a Stacked Variational Auto-Encoder (SVAE) and a multilayer perception (MLP).
|Mathematics Content Understanding for Cyberlearning via Formula Evolution Map
|Zhuoren Jiang, Liangcai Gao, Ke Yuan, Zheng Gao, Zhi Tang, Xiaozhong Liu
|In this paper, we propose a novel problem, “mathematics content understanding”, for cyberlearning and cyberreading.
|The Range Skyline Query
|Theodoros Tzouramanis, Eleftherios Tiakas, Apostolos N. Papadopoulos, Yannis Manolopoulos
|This paper considers the query as a hyper-rectangle iso-oriented towards the axes of the multi-dimensional space and proposes: (i) main-memory algorithmic strategies, which are simple to implement and (ii) secondary-memory pruning mechanisms for processing range skyline queries efficiently.
|FA + TA <FSA: Flexible Score Aggregation
|Paolo Ciaccia, Davide Martinenghi
|In this paper, we consider the problem of processing multi-source top-k queries, when only constraints, rather than precise values, are available for the weights.
|FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning
|Shuai Yang, Xipeng Shen
|This paper presents FALCON, a fast replacement of Citation KNN.
|Secure Top-k Inner Product Retrieval
|Zhilin Zhang, Ke Wang, Chen Lin, Weipeng Lin
|To enable the server-side filtering, we introduce an asymmetric inner product encryption AIPE that allows the server to compute inner products from encrypted data and query vectors.
|A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification
|Xuan Wu, Lingxiao Zhao, Leman Akoglu
|In this work we introduce a new, parallel graph learning framework (called PG-learn) for the graph construction step of SSL.
|TGNet: Learning to Rank Nodes in Temporal Graphs
|Qi Song, Bo Zong, Yinghui Wu, Lu-An Tang, Hui Zhang, Guofei Jiang, Haifeng Chen
|This paper introduces TGNet , a deep learning framework for node ranking in heterogeneous temporal graphs.
|Mining (maximal) Span-cores from Temporal Networks
|Edoardo Galimberti, Alain Barrat, Francesco Bonchi, Ciro Cattuto, Francesco Gullo
|Our first contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently.
|REGAL: Representation Learning-based Graph Alignment
|Mark Heimann, Haoming Shen, Tara Safavi, Danai Koutra
|Motivated by recent advancements in node representation learning for single-graph tasks, we propose REGAL (REpresentation learning-based Graph ALignment), a framework that leverages the power of automatically-learned node representations to match nodes across different graphs.
|Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation
|Shaoyun Shi, Min Zhang, Yiqun Liu, Shaoping Ma
|In this work, we study how to integrate CF and CB, which utilizes both types of information in model-level but not in result-level and makes recommendations adaptively.
|CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks
|Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, Jung-Tae Lee
|In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation.
|ANR: Aspect-based Neural Recommender
|Jin Yao Chin, Kaiqi Zhao, Shafiq Joty, Gao Cong
|In this paper, we propose a novel end-to-end Aspect-based Neural Recommender (ANR) to perform aspect-based representation learning for both users and items via an attention-based component.
|An Attentive Interaction Network for Context-aware Recommendations
|Lei Mei, Pengjie Ren, Zhumin Chen, Liqiang Nie, Jun Ma, Jian-Yun Nie
|In this paper, we propose a novel neural model, named Attentive Interaction Network (AIN), to enhance CARS through adaptively capturing the interactions between contexts and users/items.
|Contrasting Search as a Learning Activity with Instructor-designed Learning
|Felipe Moraes, Sindunuraga Rikarno Putra, Claudia Hauff
|Existing research has largely focused on observing how users with learning-oriented information needs behave and interact with search engines.
|Towards Conversational Search and Recommendation: System Ask, User Respond
|Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, W. Bruce Croft
|In this paper, we propose and evaluate a unified conversational search/recommendation framework, in an attempt to make the research problem doable under a standard formalization.
|Effective User Interaction for High-Recall Retrieval: Less is More
|Haotian Zhang, Mustafa Abualsaud, Nimesh Ghelani, Mark D. Smucker, Gordon V. Cormack, Maura R. Grossman
|High-recall retrieval — finding all or nearly all relevant documents — is critical to applications such as electronic discovery, systematic review, and the construction of test collections for information retrieval tasks.
|RIN: Reformulation Inference Network for Context-Aware Query Suggestion
|Jyun-Yu Jiang, Wei Wang
|In this paper, we propose Reformulation Inference Network (RIN) to learn how users reformulate queries, thereby benefiting context-aware query suggestion.
|Improving the Efficiency of Inclusion Dependency Detection
|Nuhad Shaabani, Christoph Meinel
|To this end, we propose S-indd++ as a scalable system for detecting unary INDs in large datasets.
|Web Table Understanding by Collective Inference
|San Kim, Guoliang Li, Jianhua Feng, Kaiyu Li
|To address these limitations, we propose a collective inference approach (CIA) based on Topic Sensitive PageRank, which considers not only the types of detected columns, but also the collective information of web tables to automatically produce more accurate top-k types, especially the top-1 type, for both incorrectly detected columns and undetectable columns whose cells do not exist in the knowledge base.
|A Content-Based Approach for Modeling Analytics Operators
|Ioannis Giannakopoulos, Dimitrios Tsoumakos, Nectarios Koziris
|To tackle this challenge, we propose a novel dataset profiling methodology that infers an operator’s outcome based on examining the similarity of the available input datasets in specific attributes.
|Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
|Yadan Luo, Ziwei Wang, Zi Huang, Yang Yang, Cong Zhao
|In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale.
|Shared Embedding Based Neural Networks for Knowledge Graph Completion
|Saiping Guan, Xiaolong Jin, Yuanzhuo Wang, Xueqi Cheng
|In this paper, we propose a Shared Embedding based Neural Network (SENN) model for KGC.
|Knowledge Graph Completion by Context-Aware Convolutional Learning with Multi-Hop Neighborhoods
|Byungkook Oh, Seungmin Seo, Kyong-Ho Lee
|In this paper, we propose a context-aware convolutional learning (CACL) model which jointly learns from entities and their multi-hop neighborhoods.
|Smooth q-Gram, and Its Applications to Detection of Overlaps among Long, Error-Prone Sequencing Reads
|Haoyu Zhang, Qin Zhang, Haixu Tang
|We propose smooth q-gram, the first variant of q-gram that captures q-gram pair within a small edit distance.
|Multi-View Group Anomaly Detection
|Hongtao Wang, Pan Su, Miao Zhao, Hongmei Wang, Gang Li
|In this paper, we formalize this group anomaly detection issue, and propose a novel non-parametric bayesian model, named Multi-view Group Anomaly Detection (MGAD).
|Detecting Outliers in Data with Correlated Measures
|Yu-Hsuan Kuo, Zhenhui Li, Daniel Kifer
|In this paper, we propose a new outlier detection method that utilizes the correlations in the data (e.g., taxi trip distance vs. trip time).
|Insights from the Long-Tail: Learning Latent Representations of Online User Behavior in the Presence of Skew and Sparsity
|Adit Krishnan, Ashish Sharma, Hari Sundaram
|This paper proposes an approach to learn robust behavior representations in online platforms by addressing the challenges of user behavior skew and sparse participation.
|Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information
|Madhav Nimishakavi, Bamdev Mishra, Manish Gupta, Partha Talukdar
|We bridge this gap in this paper by proposing a dynamic tensor completion framework called Side Information infused Incremental Tensor Analysis (SIITA), which incorporates side information and works for general incremental tensors.
|Online Learning for Non-Stationary A/B Tests
|Andr�s Mu�oz Medina, Sergei Vassilvitskii, Dong Yin
|In this work we formulate this question as that of expert learning, and give a new algorithm Follow-The-Best-Interval, FTBI, that works in dynamic, non-stationary environments.
|MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks
|Jing Zhang, Bo Chen, Xianming Wang, Hong Chen, Cuiping Li, Fengmei Jin, Guojie Song, Yutao Zhang
|To effectively model the influence from neighbors, we propose a graph neural network to directly represent the ego networks of two users to be aligned into an embedding, based on which we predict the alignment label.
|Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
|Donghyeon Kim, Jinhyuk Lee, Donghee Choi, Jaehoon Choi, Jaewoo Kang
|In this paper, we propose Neural Event Scheduling Assistant (NESA) which learns user preferences and understands calendar contexts, directly from raw online calendars for fully automated and highly effective event scheduling.
|Personalizing Search Results Using Hierarchical RNN with Query-aware Attention
|Songwei Ge, Zhicheng Dou, Zhengbao Jiang, Jian-Yun Nie, Ji-Rong Wen
|We propose a query-aware attention model to generate a dynamic user profile based on the input query.
|Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation
|Junliang Yu, Min Gao, Jundong Li, Hongzhi Yin, Huan Liu
|In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations.
|Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum
|Adam Tsakalidis, Nikolaos Aletras, Alexandra I. Cristea, Maria Liakata
|In this paper, we focus on the 2015 Greek bailout referendum, aiming to nowcast on a daily basis the voting intention of 2,197 Twitter users.
|Inferring Probabilistic Contagion Models Over Networks Using Active Queries
|Abhijin Adiga, Vanessa Cedeno-Mieles, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns
|We present approximation algorithms that provide provably good estimates of edge probabilities.
|Trustworthy Experimentation Under Telemetry Loss
|Jayant Gupchup, Yasaman Hosseinkashi, Pavel Dmitriev, Daniel Schneider, Ross Cutler, Andrei Jefremov, Martin Ellis
|In this paper, we take a top-down approach towards solving this problem.
|When Rank Order Isn’t Enough: New Statistical-Significance-Aware Correlation Measures
|Mucahid Kutlu, Tamer Elsayed, Maram Hasanain, Matthew Lease
|To address this, we propose two statistical-significance-aware rank correlation measures, one of which is a head-weighted version of the other.
|On Building Fair and Reusable Test Collections using Bandit Techniques
|Ellen M. Voorhees
|Various approaches for minimizing the number of judgments required have been proposed including a suite of methods based on multi-arm bandit optimization techniques.
|RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
|Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo
|To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems.
|Exploiting Structural and Temporal Evolution in Dynamic Link Prediction
|Huiyuan Chen, Jing Li
|In this work, we propose a novel framework named STEP, to simultaneously integrate both structural and temporal information in link prediction in dynamic networks.
|Are Meta-Paths Necessary?: Revisiting Heterogeneous Graph Embeddings
|Rana Hussein, Dingqi Yang, Philippe Cudr�-Mauroux
|In this paper, we propose an alternative solution that does not involve any meta-path.
|Distribution Distance Minimization for Unsupervised User Identity Linkage
|Chaozhuo Li, Senzhang Wang, Philip S. Yu, Lei Zheng, Xiaoming Zhang, Zhoujun Li, Yanbo Liang
|We propose to use the earth mover’s distance (EMD) as the measure of distribution closeness, and propose two models UUIL$_gan $ and UUIL$_omt $ to efficiently learn the distribution projection function.
|Short Text Entity Linking with Fine-grained Topics
|Lihan Chen, Jiaqing Liang, Chenhao Xie, Yanghua Xiao
|We leverage our linking approach to segment the short text semantically, and build a system for short entity text recognition and linking.
|StuffIE: Semantic Tagging of Unlabeled Facets Using Fine-Grained Information Extraction
|Radityo Eko Prasojo, Mouna Kacimi, Werner Nutt
|In this paper, we tackle the above problems by proposing StuffIE, a fine-grained information extraction approach which is facet-centric.
|PSLSH: An Index Structure for Efficient Execution of Set Queries in High-Dimensional Spaces
|Parth Nagarkar, K. Sel�uk Candan
|We propose a novel index structure, Point Set LSH (PSLSH), which is able to execute a similarity search for a given set of search points in the high-dimensional space with a user-provided guarantee for the entire set query.
|GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks
|Dhruv Gupta, Klaus Berberich
|In this work, we describe GYANI (gyan stands for knowledge in Hindi), an indexing infrastructure for search and analysis of large semantically annotated document collections.
|From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing
|Hamed Zamani, Mostafa Dehghani, W. Bruce Croft, Erik Learned-Miller, Jaap Kamps
|In this work, we propose a standalone neural ranking model (SNRM) by introducing a sparsity property to learn a latent sparse representation for each query and document.
|ChangeDAR: Online Localized Change Detection for Sensor Data on a Graph
|Bryan Hooi, Leman Akoglu, Dhivya Eswaran, Amritanshu Pandey, Marko Jereminov, Larry Pileggi, Christos Faloutsos
|Our contributions are: 1) Algorithm : we propose novel information-theoretic optimization objectives for scoring and detecting localized changes, and propose two algorithms, ChangeDAR-S and ChangeDAR-D respectively, to optimize them.
|Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction
|Qitian Wu, Chaoqi Yang, Hengrui Zhang, Xiaofeng Gao, Paul Weng, Guihai Chen
|In this paper, we propose PreNets unifying the two thinking paradigms in an adversarial manner.
|Learning under Feature Drifts in Textual Streams
|Damianos P. Melidis, Myra Spiliopoulou, Eirini Ntoutsi
|In this work, we propose an approach for handling feature drifts in textual streams.
|CRPP: Competing Recurrent Point Process for Modeling Visibility Dynamics in Information Diffusion
|Avirup Saha, Bidisha Samanta, Niloy Ganguly, Abir De
|In this paper, we propose Competing Recurrent Point Process (CRPP), a probabilistic deep machinery that unifies the nonlinear generative dynamics of a collection of diffusion processes, and inter-process competition – the two ingredients of visibility dynamics.
|Finding a Dense Subgraph with Sparse Cut
|Atsushi Miyauchi, Naonori Kakimura
|In this study, we propose an optimization model for finding a community that is densely connected internally but sparsely connected to the rest of the graph.
|Signed Network Modeling Based on Structural Balance Theory
|Tyler Derr, Charu Aggarwal, Jiliang Tang
|Hence, in this paper, we investigate the problem of modeling signed networks.
|On Rich Clubs of Path-Based Centralities in Networks
|Soumya Sarkar, Sanjukta Bhowmick, Animesh Mukherjee
|In this paper, we explore the emergence of “rich clubs” in the context of shortest path based centrality metrics.
|VTeller: Telling the Values Somewhere, Sometime in a Dynamic Network of Urban Systems
|Yan Li, Tingjian Ge, Cindy Chen
|We propose a machine learning approach using a novel probabilistic graphical model.
|Open-Schema Event Profiling for Massive News Corpora
|Quan Yuan, Xiang Ren, Wenqi He, Chao Zhang, Xinhe Geng, Lifu Huang, Heng Ji, Chin-Yew Lin, Jiawei Han
|To address these challenges, we propose a fully automatic, unsupervised, three-step framework to obtain event profiles.
|Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions
|Hong-Han Shuai, Yen-Chieh Lien, De-Nian Yang, Yi-Feng Lan, Wang-Chien Lee, Philip S. Yu
|In this paper, we argue that by mining OSN data in support of online intervention treatment, data scientists may assist mental healthcare professionals to alleviate the symptoms of users with SNA in early stages.
|Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming
|Antoine Dedieu, Rahul Mazumder, Zhen Zhu, Hossein Vahabi
|In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service.
|Question Headline Generation for News Articles
|Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Jun Xu, Huanhuan Cao, Xueqi Cheng
|In this paper, we introduce and tackle the Question Headline Generation (QHG) task.
|Relevance Estimation with Multiple Information Sources on Search Engine Result Pages
|Junqi Zhang, Yiqun Liu, Shaoping Ma, Qi Tian
|To evaluate the performance of the proposed model, we construct a large scale practical Search Result Relevance (SRR) dataset which consists of multiple information sources and 4-grade relevance scores of over 60,000 search results.
|JIM: Joint Influence Modeling for Collective Search Behavior
|Shubhra Kanti Karmaker Santu, Liangda Li, Yi Chang, ChengXiang Zhai
|In this paper, we study this novel problem of Modeling the Joint Influences posed by multiple correlated events on user search behavior.
|Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
|Kyosuke Nishida, Itsumi Saito, Atsushi Otsuka, Hisako Asano, Junji Tomita
|In this paper, we propose a simple and effective approach that incorporates the IR and RC tasks by using supervised multi-task learning in order that the IR component can be trained by considering answer spans.
|Bug Localization by Learning to Rank and Represent Bug Inducing Changes
|Pablo Loyola, Kugamoorthy Gajananan, Fumiko Satoh
|In this work, we propose a model that, instead of working at file level, learns feature representations from source changes extracted from the project history at both syntactic and code change dependency perspectives to support bug localization.
|CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
|Guangneng Hu, Yu Zhang, Qiang Yang
|In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model.
|PARL: Let Strangers Speak Out What You Like
|Libing Wu, Cong Quan, Chenliang Li, Donghong Ji
|To this end, we propose a method to exploit user-item p air-dependent features from a uxiliary r eviews written by l ike-minded users (PARL) to address such problem.
|Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
|Thanh Tran, Kyumin Lee, Yiming Liao, Dongwon Lee
|Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked.
|Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence
|Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu
|To cope with these challenges, we propose a novel autoencoder-based model to learn the complex user-POI relations, namely SAE-NAD, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD).
|Meta-Analysis for Retrieval Experiments Involving Multiple Test Collections
|We present a widely-used statistical tool, \em meta-analysis, as a framework for reporting results from IR experiments using multiple test collections.
|Presentation Ordering Effects On Assessor Agreement
|Tadele T. Damessie, J. Shane Culpepper, Jaewon Kim, Falk Scholer
|Our primary goal is to determine if assessor disagreement can be minimized through the order in which documents are presented to assessors.
|Understanding Reading Attention Distribution during Relevance Judgement
|Xiangsheng Li, Yiqun Liu, Jiaxin Mao, Zexue He, Min Zhang, Shaoping Ma
|In this paper, we focus on the reading process during relevance judgement task.
|KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare
|Fenglong Ma, Quanzeng You, Houping Xiao, Radha Chitta, Jing Zhou, Jing Gao
|To address these issues, we propose KAME, an end-to-end, accurate and robust model for predicting patients’ future health information.
|“Let Me Tell You About Your Mental Health!”: Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention
|Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Jyotishman Pathak
|Specifically, we provide a detailed analysis of the nature of subreddit content from domain expert’s perspective and introduce a novel approach to map each subreddit to the best matching DSM-5 (Diagnostic and Statistical Manual of Mental Disorders – 5th Edition) category using multi-class classifier.
|HeteroMed: Heterogeneous Information Network for Medical Diagnosis
|Anahita Hosseini, Ting Chen, Wenjun Wu, Yizhou Sun, Majid Sarrafzadeh
|To address these issues, we represent how high-dimensional EHR data and its rich relationships can be suitably translated into HeteroMed, a heterogeneous information network for robust medical diagnosis.
|“Bridge”: Enhanced Signed Directed Network Embedding
|Yiqi Chen, Tieyun Qian, Huan Liu, Ke Sun
|In this paper, we investigate the problem of learning representations for signed directed networks.
|Semi-Supervised Multi-Label Feature Selection by Preserving Feature-Label Space Consistency
|Yuanyuan Xu, Jun Wang, Shuai An, Jinmao Wei, Jianhua Ruan
|In this paper, we propose a space consistency-based feature selection model to address this issue.
|COPA: Constrained PARAFAC2 for Sparse & Large Datasets
|Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis, Elizabeth Searles, Joyce Ho, Jimeng Sun
|To tackle these challenges, we propose a COnstrained PARAFAC2 (COPA) method, which carefully incorporates optimization constraints such as temporal smoothness, sparsity, and non-negativity in the resulting factors.
|The Impact of Name-Matching and Blocking on Author Disambiguation
|In this work, we address the problem of blocking in the context of author name disambiguation.
|Parallel Hashing Using Representative Points in Hyperoctants
|Chaomin Shen, Mixue Yu, Chenxiao Zhao, Yaxin Peng, Guixu Zhang
|To combine their advantages and avoid their drawbacks, we propose a novel algorithm, termed as representative points quantization (RPQ), by using the representative points defined as the barycenters of points in the hyperoctants.
|PRRE: Personalized Relation Ranking Embedding for Attributed Networks
|Sheng Zhou, Hongxia Yang, Xin Wang, Jiajun Bu, Martin Ester, Pinggang Yu, Jianwei Zhang, Can Wang
|In this paper, we take partial correlation between topology and attributes into account and propose the Personalized Relation Ranking Embedding (PRRE) method for attributed networks which is capable of exploiting the partial correlation between node topology and attributes.
|Heterogeneous Neural Attentive Factorization Machine for Rating Prediction
|Liang Chen, Yang Liu, Zibin Zheng, Philip Yu
|Inspired by these work, in this paper, we propose Heterogeneous Neural Attentive Factorization Machine(HNAFM) to solve above problems.
|Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
|Bal�zs Hidasi, Alexandros Karatzoglou
|In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting.
|Interactions Modeling in Multi-Task Multi-View Learning with Consistent Task Diversity
|Xiaoli Li, Jun Huan
|To remedy this, we propose a novel method, racBFA, by adding rank constraints to asymmetric bilinear factor analyzers (aBFA).
|Distinguishing Trajectories from Different Drivers using Incompletely Labeled Trajectories
|Tung Kieu, Bin Yang, Chenjuan Guo, Christian S. Jensen
|We consider a scenario that occurs often in the auto insurance industry.
|Modeling Sequential Online Interactive Behaviors with Temporal Point Process
|Renqin Cai, Xueying Bai, Zhenrui Wang, Yuling Shi, Parikshit Sondhi, Hongning Wang
|In this paper, we study the problem by looking into users’ sequential interactive behaviors.
|Towards Practical Open Knowledge Base Canonicalization
|Tien-Hsuan Wu, Zhiyong Wu, Ben Kao, Pengcheng Yin
|We propose the FAC algorithm for solving the canonicalization problem.
|Semantically-Enhanced Topic Modeling
|Felipe Viegas, Washington Luiz, Christian Gomes, Amir Khatibi, S�rgio Canuto, Fernando Mour�o, Thiago Salles, Leonardo Rocha, Marcos Andr� Gon�alves
|In this paper, we advance the state-of-the-art in topic modeling by means of the design and development of a novel (semi-formal) general topic modeling framework.
|METIC: Multi-Instance Entity Typing from Corpus
|Bo Xu, Zheng Luo, Luyang Huang, Bin Liang, Yanghua Xiao, Deqing Yang, Wei Wang
|In this paper, we therefore propose to use the text corpus of an entity to infer its types, and propose a multi-instance method to tackle this problem.
|Semi-supervised Learning on Graphs with Generative Adversarial Nets
|Ming Ding, Jie Tang, Jie Zhang
|We investigate how generative adversarial nets (GANs) can help semi-supervised learning on graphs.
|Mining Frequent Patterns in Evolving Graphs
|Cigdem Aslay, Muhammad Anis Uddin Nasir, Gianmarco De Francisci Morales, Aristides Gionis
|In this paper, we initiate the study of the approximate FSM problem in both incremental and fully-dynamic streaming settings, where arbitrary edges can be added or removed from the graph.
|Multiresolution Graph Attention Networks for Relevance Matching
|Ting Zhang, Bang Liu, Di Niu, Kunfeng Lai, Yu Xu
|In this paper, we are especially interested in relevance matching between a piece of short text and a long document, which is critical to problems like query-document matching in information retrieval and web searching.
|Rumor Detection with Hierarchical Social Attention Network
|Han Guo, Juan Cao, Yazi Zhang, Junbo Guo, Jintao Li
|In this paper, we detect rumors by leveraging hierarchical representations at different levels and the social contexts.
|Modeling Users’ Exposure with Social Knowledge Influence and Consumption Influence for Recommendation
|Jiawei Chen, Yan Feng, Martin Ester, Sheng Zhou, Chun Chen, Can Wang
|In this paper, we propose a novel social exposure-based recommendation model SoEXBMF by integrating two kinds of social influence on users’ exposure, i.e. social knowledge influence and social consumption influence, into basic EXMF model for better recommendation performance.
|Exploring People’s Attitudes and Behaviors Toward Careful Information Seeking in Web Search
|Takehiro Yamamoto, Yusuke Yamamoto, Sumio Fujita
|The goal of this study is to better understand people’s attitudes toward careful information seeking via Web search, and the relationship between such attitudes and their daily search behaviors.
|Dataless Text Classification: A Topic Modeling Approach with Document Manifold
|Ximing Li, Changchun Li, Jinjin Chi, Jihong Ouyang, Chenliang Li
|In this paper, we address these issues using document manifold, assuming that neighboring documents tend to be assigned to a same category label.
|Weakly-Supervised Neural Text Classification
|Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han
|In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification.
|Creating Scoring Rubric from Representative Student Answers for Improved Short Answer Grading
|Smit Marvaniya, Swarnadeep Saha, Tejas I. Dhamecha, Peter Foltz, Renuka Sindhgatta, Bikram Sengupta
|In this paper, we propose an affinity propagation based clustering technique to obtain class-specific representative answers from the graded student answers.
|Probabilistic Causal Analysis of Social Influence
|Francesco Bonchi, Francesco Gullo, Bud Mishra, Daniele Ramazzotti
|In this paper we adopt a principled causal approach to the analysis of social influence from information-propagation data, rooted in the theory of probabilistic causation.
|Adversarial Learning of Answer-Related Representation for Visual Question Answering
|Yun Liu, Xiaoming Zhang, Feiran Huang, Zhoujun Li
|To address this problem, this paper proposes a novel method, i.e., Adversarial Learning of Answer-Related Representation (ALARR) for visual question answering, which seeks an effective answer-related representation for the question-image pair based on adversarial learning between two processes.
|Stochastic Coupon Probing in Social Networks
|We can offer coupons to some users adaptively and those users who accept the offer will act as seeds and influence their friends in the social network.
|Type Prediction Combining Linked Open Data and Social Media
|Yaroslav Nechaev, Francesco Corcoglioniti, Claudio Giuliano
|In this paper, we investigate the feasibility of using social media data to perform type prediction for entities in a LOD knowledge graph.
|Efficient and Reliable Estimation of Cell Positions
|Mirela T. Cazzolato, Agma J. M. Traina, Klemens B�hm
|In this work we propose CM-Predictor, which takes advantage of previous positions of cells to estimate their motion.
|On Real-time Detecting Passenger Flow Anomalies
|Bo Tang, Hongyin Tang, Xinzhou Dong, Beihong Jin, Tingjian Ge
|In this paper, we devise an approach called Kochab. In addition, for the convenience of method evaluation and comparison, we create an open Stream Anomaly Benchmark on the basis of large-scale real-world data.
|A Novel Online Stacked Ensemble for Multi-Label Stream Classification
|Alican B�y�k�akir, Hamed Bonab, Fazli Can
|In this study, we introduce a novel online and dynamically-weighted stacked ensemble for multi-label classification, called GOOWE-ML, that utilizes spatial modeling to assign optimal weights to its component classifiers.
|RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data
|Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Louis Faust, Nitesh V. Chawla
|To fully exploit these underlying dynamics, this paper studies the forecasting problem for behavioral time series data with the consideration of multiple time resolutions and proposes a multi-resolution time series forecasting framework, RESolution-aware Time series Forecasting (RESTFul).
|Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range
|Jun-Gi Jang, Dongjin Choi, Jinhong Jung, U Kang
|In this paper, we propose Zoom-SVD, a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range.
|Disk-based Matrix Completion for Memory Limited Devices
|Dongha Lee, Jinoh Oh, Christos Faloutsos, Byungju Kim, Hwanjo Yu
|This paper proposes D-MC2, a novel disk-based matrix completion method that (1) supports incremental data update (i.e., data insertion and deletion) and (2) spills both data and model to disk when necessary; these functionalities are not supported by existing methods.
|Naive Parallelization of Coordinate Descent Methods and an Application on Multi-core L1-regularized Classification
|Yong Zhuang, Yuchin Juan, Guo-Xun Yuan, Chih-Jen Lin
|For almost all real-world sparse sets we have examined, some features are much denser than others.
|CUSNTF: A Scalable Sparse Non-negative Tensor Factorization Model for Large-scale Industrial Applications on Multi-GPU
|Hao Li, Kenli Li, Jiyao An, Keqin Li
|We implement CUSNTF and MCUSNTF on 8 P100 GPUs, and compare it with state-of-the-art parallel and distributed methods.
|DiVE: Diversifying View Recommendation for Visual Data Exploration
|Rischan Mafrur, Mohamed A. Sharaf, Hina A. Khan
|To address that limitation, in this work we posit that employing diversification techniques in the process of view recommendation allows eliminating that redundancy and provides a good and concise coverage of the possible insights to be discovered.
|Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty
|Mengting Wan, Di Wang, Jie Liu, Paul Bennett, Julian McAuley
|We study the problem of representing and recommending products for grocery shopping.
|Recommendation Through Mixtures of Heterogeneous Item Relationships
|Wang-Cheng Kang, Mengting Wan, Julian McAuley
|Here we seek to develop a framework that is capable of combining such heterogeneous item relationships by simultaneously modeling (a) what modality of recommendation is a user likely to be susceptible to at a particular point in time; and (b) what is the best recommendation from each modality.
|Fairness-Aware Tensor-Based Recommendation
|Ziwei Zhu, Xia Hu, James Caverlee
|Hence, we propose a novel fairness-aware tensor recommendation framework that is designed to maintain quality while dramatically improving fairness.
|Generating Keyword Queries for Natural Language Queries to Alleviate Lexical Chasm Problem
|Xiaoyu Liu, Shunda Pan, Qi Zhang, Yu-Gang Jiang, Xuanjing Huang
|In this work, we formulated the task as a translation problem to convert natural language queries into keyword queries.
|Attentive Neural Architecture for Ad-hoc Structured Document Retrieval
|Saeid Balaneshinkordan, Alexander Kotov, Fedor Nikolaev
|In this paper, we propose a deep neural architecture for ad-hoc structured document retrieval, which utilizes attention mechanism to determine important phrases in keyword queries as well as the relative importance of matching those phrases in different fields of structured documents.
|Measuring User Satisfaction on Smart Speaker Intelligent Assistants Using Intent Sensitive Query Embeddings
|Seyyed Hadi Hashemi, Kyle Williams, Ahmed El Kholy, Imed Zitouni, Paul A. Crook
|In this paper, we propose a new signal, user intent, as a means to measure user satisfaction.
|Impact of Domain and User’s Learning Phase on Task and Session Identification in Smart Speaker Intelligent Assistants
|Seyyed Hadi Hashemi, Kyle Williams, Ahmed El Kholy, Imed Zitouni, Paul A. Crook
|In this study, we investigate how to identify tasks and sessions in IAs given these differences.
|Engineering a Simplified 0-Bit Consistent Weighted Sampling
|Edward Raff, Jared Sylvester, Charles Nicholas
|We develop a new Simplified approach to the ICWS algorithm, that enables us to obtain over 20x speedups compared to the standard algorithm.
|A Scalable Algorithm for Higher-order Features Generation using MinHash
|Pooja A, Naveen Nair, Rajeev Rastogi
|In this paper, we propose a novel scalable MinHash based scheme to select informative higher-order features.
|Multiperspective Graph-Theoretic Similarity Measure
|Dung D. Le, Hady W. Lauw
|In this work, we propose a graph-theoretic similarity measure that is natively multiperspective.
|Explicit Preference Elicitation for Task Completion Time
|Mohammadreza Esfandiari, Senjuti Basu Roy, Sihem Amer-Yahia
|We initiate a study that leverages explicit elicitation from workers to capture the evolving nature of worker preferences and we propose an optimization framework to better understand and estimate task completion time.
|Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization
|Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Yu Zheng, Christina Kirsch
|In this paper, we propose three online non-negative matrix factorization (ONMF) models.
|Studying Topical Relevance with Evidence-based Crowdsourcing
|Oana Inel, Giannis Haralabopoulos, Dan Li, Christophe Van Gysel, Zolt�n Szl�vik, Elena Simperl, Evangelos Kanoulas, Lora Aroyo
|In this paper we study the topical relevance from a user perspective by addressing the problems of subjectivity and ambiguity.
|Randomized Bit Vector: Privacy-Preserving Encoding Mechanism
|Lin Sun, Lan Zhang, Xiaojun Ye
|In this paper, we propose distance-aware encoding mechanisms to compare numerical values in the anonymous space.
|Privacy Protection for Flexible Parametric Survival Models
|Th�ng T. Nguy?n, Siu Cheung Hui
|In this work, we propose two solutions to the privacy-preserving problem of regression models on medical data.
|Privacy-Preserving Triangle Counting in Large Graphs
|Xiaofeng Ding, Xiaodong Zhang, Zhifeng Bao, Hai Jin
|In this paper, we choose to use differential privacy to protect triangle counting for large scale graphs.
|Differentiable Unbiased Online Learning to Rank
|Harrie Oosterhuis, Maarten de Rijke
|We introduce an entirely novel approach to OLTR that constructs a weighted differentiable pairwise loss after each interaction: Pairwise Differentiable Gradient Descent (PDGD).
|A Quantum Many-body Wave Function Inspired Language Modeling Approach
|Peng Zhang, Zhan Su, Lipeng Zhang, Benyou Wang, Dawei Song
|To address these two issues, in this paper, we propose a Quantum Many-body Wave Function (QMWF) inspired language modeling approach.
|The LambdaLoss Framework for Ranking Metric Optimization
|Xuanhui Wang, Cheng Li, Nadav Golbandi, Michael Bendersky, Marc Najork
|In this paper, we present LambdaLoss, a probabilistic framework for ranking metric optimization.
|PolyHJ: A Polymorphic Main-Memory Hash Join Paradigm for Multi-Core Machines
|Omar Khattab, Mohammad Hammoud, Omar Shekfeh
|In this paper, we show that different input features and hardware settings necessitate different main-memory hash join models.
|When Optimizer Chooses Table Scans: How to Make Them More Responsive
|Lijian Wan, Tingjian Ge
|We formulate it as a query result timeliness problem, and propose two complementary approaches.
|Construction of Efficient V-Gram Dictionary for Sequential Data Analysis
|Igor Kuralenok, Natalia Starikova, Aleksandr Khvorov, Julian Serdyuk
|This paper presents a new method for constructing an optimal feature set from sequential data.
|Neural Collaborative Ranking
|Bo Song, Xin Yang, Yi Cao, Congfu Xu
|In this paper, we examine an alternative approach which does not assume that the non-interacted items are necessarily negative, just that they are less preferred than interacted items.
|Collaborative Multi-objective Ranking
|Jun Hu, Ping Li
|In this paper, we demonstrate that by individually solving row-wise or column-wise ranking problems using typical CR algorithms is only able to learn one set of effective (user or item) latent factors.
|Mix ‘n Match: Integrating Text Matching and Product Substitutability within Product Search
|Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
|We introduce a method for intrinsically incorporating product substitutability within latent vector space models for product search that are estimated using gradient descent; it integrates flawlessly with state-of-the-art vector space models.
|In Situ and Context-Aware Target Apps Selection for Unified Mobile Search
|Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft
|In this paper, we introduce the task of context-aware target apps selection as part of a unified mobile search framework.
|Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection
|Fanghua Ye, Chuan Chen, Zibin Zheng
|Inspired by the unique feature representation learning capability of deep autoencoder, we propose a novel model, named Deep Autoencoder-like NMF (DANMF), for community detection.
|Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation
|Xisen Jin, Wenqiang Lei, Zhaochun Ren, Hongshen Chen, Shangsong Liang, Yihong Zhao, Dawei Yin
|In this paper, we propose the semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation.
|On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning based Approach
|Jing Zhao, Jiajie Xu, Rui Zhou, Pengpeng Zhao, Chengfei Liu, Feng Zhu
|This paper presents a carefully designed deep learning model called TALL model for destination prediction.
|DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction
|Chao Huang, Junbo Zhang, Yu Zheng, Nitesh V. Chawla
|In this paper, we develop a new crime prediction framework–DeepCrime, a deep neural network architecture that uncovers dynamic crime patterns and carefully explores the evolving inter-dependencies between crimes and other ubiquitous data in urban space.
|Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising
|Kan Ren, Yuchen Fang, Weinan Zhang, Shuhao Liu, Jiajun Li, Ya Zhang, Yong Yu, Jun Wang
|In this paper, we propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch attribution problem.
|Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
|Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, Kun Gai
|To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem.
|Deep Semantic Hashing with Multi-Adversarial Training
|Bingning Wang, Kang Liu, Jun Zhao
|In this paper, to generate desirable binary codes, we introduce two adversarial training procedures to the hashing process.
|Communication-Efficient Distributed Deep Metric Learning with Hybrid Synchronization
|Yuxin Su, Michael Lyu, Irwin King
|In this paper, we introduce a novel distributed framework to speed up the training process of the deep metric learning using multiple machines.
|A Dynamical System on Bipartite Graphs
|Kishore Papineni, Pratik Worah
|This paper poses a non-linear dynamical system on bipartite graphs and shows its stability under certain conditions.
|Abnormal Event Detection via Heterogeneous Information Network Embedding
|Shaohua Fan, Chuan Shi, Xiao Wang
|In this paper, we propose a novel deep heterogeneous network embedding method which incorporates the entity attributes and second-order structures simultaneously to address this problem.
|AceKG: A Large-scale Knowledge Graph for Academic Data Mining
|Ruijie Wang, Yuchen Yan, Jialu Wang, Yuting Jia, Ye Zhang, Weinan Zhang, Xinbing Wang
|In this paper, we present AceKG, a new large-scale KG in academic domain.
|An Adversarial Approach to Improve Long-Tail Performance in Neural Collaborative Filtering
|Adit Krishnan, Ashish Sharma, Aravind Sankar, Hari Sundaram
|In this paper, we propose a novel adversarial training strategy to enhance long-tail recommendations for users with Neural CF (NCF) models.
|AQuPR: Attention based Query Passage Retrieval
|Parth Pathak, Mithun Das Gupta, Niranjan Nayak, Harsh Kohli
|With the advent of machine reading comprehension techniques, Web search is moving more towards identifying the best sentence / group of sentences in the document. We collect a database of human issued queries along with their answer passages and learn an end to end system to enable automated query resolution.
|Attentive Encoder-based Extractive Text Summarization
|Chong Feng, Fei Cai, Honghui Chen, Maarten de Rijke
|We propose an attentive encoder-based summarization (AES) model to generate article summaries.
|Calibration: A Simple Way to Improve Click Models
|Alexey Borisov, Julia Kiseleva, Ilya Markov, Maarten de Rijke
|To repair this discrepancy, we adapt a non-parametric calibration method called isotonic regression.
|Can User Behaviour Sequences Reflect Perceived Novelty?
|Mengdie Zhuang, Elaine G. Toms, Gianluca Demartini
|In this work, we investigate the relationship between user behavioural actions and perceived novelty in the context of browsing.
|Causal Dependencies for Future Interest Prediction on Twitter
|Negar Arabzadeh, Hossein Fani, Fattane Zarrinkalam, Ahmed Navivala, Ebrahim Bagheri
|In this paper, we propose that instead of considering the whole user base within a collaborative filtering framework to predict user interests, it is possible to much more accurately predict such interests by only considering the behavioral patterns of the most influential users related to the user of interest.
|Challenges of Multileaved Comparison in Practice: Lessons from NTCIR-13 OpenLiveQ Task
|Makoto P. Kato, Tomohiro Manabe, Sumio Fujita, Akiomi Nishida, Takehiro Yamamoto
|To cope with these problems in large-scale multileaved comparison, we propose a new experimental design that evaluates all the rankers online but intensively tests only the top-k rankers.
|Compiling Questions into Balanced Quizzes about Documents
|Cristina Menghini, Jessica Dehler Zufferey, Robert West
|We provide algorithms for constructing the graph and for selecting a good set of quiz questions.
|Continuation Methods and Curriculum Learning for Learning to Rank
|Nicola Ferro, Claudio Lucchese, Maria Maistro, Raffaele Perego
|In this paper we explore the use of Continuation Methods and Curriculum Learning techniques in the area of Learning to Rank.
|Correlated Time Series Forecasting using Multi-Task Deep Neural Networks
|Razvan-Gabriel Cirstea, Darius-Valer Micu, Gabriel-Marcel Muresan, Chenjuan Guo, Bin Yang
|To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
|Cross-domain Aspect/Sentiment-aware Abstractive Review Summarization
|Min Yang, Qiang Qu, Jia Zhu, Ying Shen, Zhou Zhao
|This study takes the lead to study the aspect/sentiment-aware abstractive review summarization in domain adaptation scenario.
|Data Structure for Efficient Line of Sight Queries
|Swapnil Gaikwad, Melody Moh, David C. Anastasiu
|In this paper, we develop and compare methods for verifying the Line of Sight (LOS) constraint between two points in a city.
|Detecting Parkinson’s Disease from Interactions with a Search Engine: Is Expert Knowledge Sufficient?
|Liron Allerhand, Brit Youngmann, Elad Yom-Tov, David Arkadir
|The main challenge we address is the extraction of informative features from raw mouse tracking data.
|DualBoost: Handling Missing Values with Feature Weights and Weak Classifiers that Abstain
|Weihong Wang, Jie Xu, Yang Wang, Chen Cai, Fang Chen
|In this paper we proposed a unified Boosting framework that consolidates model construction and missing value handling.
|An Effective Approach for Modelling Time Features for Classifying Bursty Topics on Twitter
|Anjie Fang, Iadh Ounis, Craig MacDonald, Philip Habel, Xiaoyu Xiong, Hai-Tao Yu
|In this paper, we propose a novel approach to use time features to predict bursty topics on Twitter.
|Efficient and Effective Query Expansion for Web Search
|Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Roberto Trani, Rossano Venturini
|The goal of this paper is to enable QE in scenarios with tight time constraints proposing a QE framework based on structured queries and efficiency-aware term selection strategies.
|Efficient Energy Management in Distributed Web Search
|Matteo Catena, Ophir Frieder, Nicola Tonellotto
|Recently several approaches to reduce the energy consumption of WSEs have been proposed.
|Efficient Pipeline Processing of Crowdsourcing Workflows
|Ken Mizusawa, Keishi Tajima, Masaki Matsubara, Toshiyuki Amagasa, Atsuyuki Morishima
|This paper addresses the pipeline processing of sequential workflows in crowdsourcing.
|Efficient Taxonomic Similarity Joins with Adaptive Overlap Constraint
|Pengfei Xu, Jiaheng Lu
|In this paper, we leverage the taxonomy knowledge (i.e., a set of IS-A hierarchical relations) to define a similarity measure which finds semantic-similar records from two datasets.
|Embedding Fuzzy K-Means with Nonnegative Spectral Clustering via Incorporating Side Information
|Muhan Guo, Rui Zhang, Feiping Nie, Xuelong Li
|In this paper, we present a novel and robust fuzzy K-Means clustering algorithm, namely Embedding Fuzzy K-Means with Nonnegative Spectral Clustering via Incorporating Side Information.
|Empirical Evidence for Search Effectiveness Models
|Alfan Farizki Wicaksono, Alistair Moffat
|Empirical Evidence for Search Effectiveness Models
|An Encoder-Memory-Decoder Framework for Sub-Event Detection in Social Media
|Guandan Chen, Nan Xu, Weiji Mao
|To overcome these drawbacks in previous research, in this paper, we propose an encoder-memory-decoder framework for sub-event detection.
|Enhanced Network Embeddings via Exploiting Edge Labels
|Haochen Chen, Xiaofei Sun, Yingtao Tian, Bryan Perozzi, Muhao Chen, Steven Skiena
|In this work, we attempt to learn network embeddings which simultaneously preserve network structure and relations between nodes.
|Enhancing Graph Kernels via Successive Embeddings
|Giannis Nikolentzos, Michalis Vazirgiannis
|In this paper, we propose to perform a series of successive embeddings in order to improve the performance of existing graph kernels and derive more expressive kernels.
|Estimating Clickthrough Bias in the Cascade Model
|Praveen Chandar, Ben Carterette
|In this work, we show that the existing counterfactual estimators fail to capture one type of bias, specifically, the effect on click-through rates due to the relevance of documents ranked above.
|Exploring Neural Translation Models for Cross-Lingual Text Similarity
|This paper explores a neural network-based approach to computing similarity of two texts written in different languages.
|Extracting Figures and Captions from Scientific Publications
|Pengyuan Li, Xiangying Jiang, Hagit Shatkay
|In this paper, we introduce a new and effective system for figure and caption extraction, PDFigCapX.
|FactCheck: Validating RDF Triples Using Textual Evidence
|Zafar Habeeb Syed, Michael R�der, Axel-Cyrille Ngonga Ngomo
|In this paper, we employ sentence coherence features gathered from trustworthy source documents to outperform the state of the art in fact checking.
|Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News
|Qikai Liu, Xiang Cheng, Sen Su, Shuguang Zhu
|In this paper, taking advantage of neural representation learning, we propose a hierarchical complementary attention network (HCAN) to capture valuable complementary information in news title and content for stock movement prediction.
|Holistic Crowd-Powered Sorting via AID: Optimizing for Accuracies, Inconsistencies, and Difficulties
|Shreya Rajpal, Aditya Parameswaran
|Our key contribution is a novel method of encoding difficulty of comparisons in the form of constraints on edges.
|Homepage Augmentation by Predicting Links in Heterogenous Networks
|Jianming Lv, Jiajie Zhong, Weihang Chen, Qinzhe Xiao, Zhenguo Yang, Qing Li
|In this paper, we propose a homepage augmentation technique, which automatically shows the newest academic events related to a scholar on his/her homepage.
|How Consistent is Relevance Feedback in Exploratory Search?
|Alan Medlar, Dorota Glowacka
|Reinforcement learning is used to build a model of user intent based on relevance feedback provided by the user.
|HRAM: A Hybrid Recurrent Attention Machine for News Recommendation
|Dhruv Khattar, Vaibhav Kumar, Vasudeva Varma, Manish Gupta
|In order to address these issues for news recommendation we propose a Hybrid Recurrent Attention Machine (HRAM).
|A Hybrid Approach for Automatic Model Recommendation
|Roman Vainshtein, Asnat Greenstein-Messica, Gilad Katz, Bracha Shapira, Lior Rokach
|We present AutoDi, a novel and resource-efficient approach for model selection.
|Hybrid Deep Sequential Modeling for Social Text-Driven Stock Prediction
|Huizhe Wu, Wei Zhang, Weiwei Shen, Jun Wang
|To bridge this gap, we propose a novel Cross-modal attention based Hybrid Recurrent Neural Network (CH-RNN), which is inspired by the recent proposed DA-RNN model.
|Imbalanced Sentiment Classification with Multi-Task Learning
|Fangzhao Wu, Chuhan Wu, Junxin Liu
|In this paper, we propose an effective approach for imbalanced sentiment classification.
|Impact of Document Representation on Neural Ad hoc Retrieval
|Ebrahim Bagheri, Faezeh Ensan, Feras Al-Obeidat
|In this paper, we propose that document representation methods need to be used to address the size imbalance problem and empirically show their impact on the performance of neural embedding-based ad hoc retrieval.
|Implementation Notes for the Soft Cosine Measure
|In this paper, we prove a tighter lower worst-case time complexity bound of O(n^3).
|Improve Network Embeddings with Regularization
|Yi Zhang, Jianguo Lu, Ofer Shai
|In this paper, we show that these algorithms suffer from norm convergence problem, and propose to use L2 regularization to rectify the problem.
|Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions
|Jasper Linmans, Bob van de Velde, Evangelos Kanoulas
|By leveraging the semantic properties of word embeddings we robustly improve on existing controversy detection methods.
|Improving Low-Rank Matrix Completion with Self-Expressiveness
|Minsu Kwon, Han-Gyu Kim, Ho-Jin Choi
|In this paper, we improve the low-rank matrix completion algorithm by assuming that the data points lie in a union of low dimensional subspaces.
|Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction
|Yingmei Chen, Zhongyu Wei, Xuanjing Huang
|In this paper, we propose to incorporate information of related corporations of a target company for its stock price prediction.
|IntentsKB: A Knowledge Base of Entity-Oriented Search Intents
|Dar�o Garigliotti, Krisztian Balog
|The main contribution of this paper is a pipeline of components we develop to construct a knowledge base of entity intents.
|Joint Dictionary Learning and Semantic Constrained Latent Subspace Projection for Cross-Modal Retrieval
|Jianlong Wu, Zhouchen Lin, Hongbin Zha
|In this paper, we present a novel joint dictionary learning and semantic constrained latent subspace learning method for cross-modal retrieval~(JDSLC) to deal with above two issues.
|K-core Minimization: An Edge Manipulation Approach
|Weijie Zhu, Chen Chen, Xiaoyang Wang, Xuemin Lin
|In this paper, we propose a novel problem, called k-core minimization.
|Label Propagation with Neural Networks
|Aditya Pal, Deepayan Chakrabarti
|We propose an algorithm called LPNN that solves these problems by a loose-coupling of LP with a feature-based classifier.
|Learning to Geolocalise Tweets at a Fine-Grained Level
|Jorge David Gonzalez Paule, Yashar Moshfeghi, Craig Macdonald, Iadh Ounis
|In this work, we adopt a learning to rank approach towards improving the effectiveness of the ranking and increasing the accuracy of fine-grained geolocalisation. To this end we propose a set of features extracted from pairs of geotagged tweets generated within the same fine-grained geographical area (squared areas of size 1 km).
|Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
|Vineeth Rakesh, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, Huan Liu
|In this paper, we aim to infer the causation that results in spillover effects between pairs of entities (or units); we call this effect as paired spillover.
|Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network
|Binbin Hu, Chuan Shi, Wayne Xin Zhao, Tianchi Yang
|To address these issues, we propose a unified model LGRec to fuse local and global information for top-N recommendation in HIN.
|Long-Term RNN: Predicting Hazard Function for Proactive Maintenance of Water Mains
|Bin Liang, Zhidong Li, Yang Wang, Fang Chen
|We apply our model to the proactive maintenance problem using a large dataset from a water utility in Australia.
|Low-Complexity Supervised Rank Fusion Models
|Andr� Mour�o, Jo�o Magalh�es
|In this paper, we investigate an approach for the selection and fusion of rank lists with low-complexity models.
|Mining & Summarizing E-petitions for Enhanced Understanding of Public Opinion
|Shreshtha Mundra, Sachin Kumar, Manjira Sinha, Sandya Mannarswamy
|To alleviate these challenges, we present an end to end system for generating comprehensive and concise summaries from e-petitions. We also introduce a new annotated petition dataset, developed through crowd-sourcing, that served as gold standard.
|MM: A new Framework for Multidimensional Evaluation of Search Engines
|Joao Palotti, Guido Zuccon, Allan Hanbury
|In this paper, we proposed a framework to evaluate information retrieval systems in presence of multidimensional relevance.
|Modeling Consumer Buying Decision for Recommendation Based on Multi-Task Deep Learning
|Qiaolin Xia, Peng Jiang, Fei Sun, Yi Zhang, Xiaobo Wang, Zhifang Sui
|In this paper, we try to bridge the gap and improve recommendation systems by explicitly modeling consumer buying decision process and corresponding stages.
|Modeling Multi-way Relations with Hypergraph Embedding
|Chia-An Yu, Ching-Lun Tai, Tak-Shing Chan, Yi-Hsuan Yang
|Inspired by Laplacian tensors of uniform hypergraphs, we propose in this paper a novel method that incorporates multi-way relations into an optimization problem.
|More than Threads: Identifying Related Email Messages
|Noa Avigdor-Elgrabli, Roei Gelbhart, Irena Grabovitch-Zuyev, Ariel Raviv
|We study the notion of semantic relatedness between email messages and aim to offer the user with a wider context of the message he selects or reads.
|MultiE: Multi-Task Embedding for Knowledge Base Completion
|Zhao Zhang, Fuzhen Zhuang, Zheng-Yu Niu, Deqing Wang, Qing He
|Along this line, we propose a novel KBC model by Multi -Task E mbedding, named MultiE.
|Multi-Emotion Category Improving Embedding for Sentiment Classification
|Shuo Wang, Xiaofeng Meng
|Instead of making a new word embedding model, we introduce the multi-emotion category (MEC) model to improve the pre-trained word vectors which aims to move target word vectors closer to the words from both similar semantics and similar emotions.
|Multiple Manifold Regularized Sparse Coding for Multi-View Image Clustering
|Xiaofei Zhu, Khoi Duy Vo, Jiafeng Guo, Jiangwu Long
|In this paper, we argue that it is more reasonable to utilize the high-level manifold consensus rather than the low-level coefficient matrix consensus to better capture the underlying clustering structure of the data.
|Multiple Pairwise Ranking with Implicit Feedback
|Runlong Yu, Yunzhou Zhang, Yuyang Ye, Le Wu, Chao Wang, Qi Liu, Enhong Chen
|In this paper, we propose a Multiple Pairwise Ranking (MPR) approach, which relaxes the simple pairwise preference assumption in previous works by further tapping the connections among items with multiple pairwise ranking criteria.
|Neighborhood Voting: A Novel Search Scheme for Hashing
|Yan Xiao, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng
|To address this issue, we propose to introduce the k-nearest neighbors (k-NNs) in the original space into the Hamming space (i.e., associating a binary code with its original k-NNs) to enhance the effectiveness of existing hashing techniques with little overhead.
|A Network-embedding Based Method for Author Disambiguation
|Jun Xu, Siqi Shen, Dongsheng Li, Yongquan Fu
|In this work, we propose a network-embedding based method for author disambiguation.
|Neural Retrieval with Partially Shared Embedding Spaces
|Bo Li, Le Jia
|We argue that queries and documents should be mapped into different but overlapping embedding spaces, which is named Partially Shared Embedding Space (PSES) model in this paper.
|An Option Gate Module for Sentence Inference on Machine Reading Comprehension
|Xuming Lin, Ruifang Liu, Yiwei Li
|In this paper, we propose an option gate approach for reading comprehension.
|Point Symmetry-based Deep Clustering
|Jose G. Moreno
|This paper presents an adaptation of symmetry-based distances into deep clustering algorithms, named SymDEC.
|Predicting Personal Life Events from Streaming Social Content
|Maryam Khodabakhsh, Hossein Fani, Fattane Zarrinkalam, Ebrahim Bagheri
|In this paper, we take a step forward and explore the possibility of predicting users’ next personal life event based solely on the their historically reported personal life events, a task which we refer to as personal life event prediction.
|Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective
|Yunlun Yang, Yu Gong, Xi Chen
|In this work, we define the real-world problem of query tracking in E-commerce conversational search, in which the goal is to update the internal query after each round of interaction. Further more we build a novel E-commerce query tracking dataset from an operational E-commerce Search Engine, and experimental results on this dataset suggest that our proposed model outperforms several baseline methods by a substantial gain for Exact Match accuracy and F1 score, showing the potential of machine comprehension like model for this task.
|Query Understanding via Entity Attribute Identification
|Arash Dargahi Nobari, Arian Askari, Faegheh Hasibi, Mahmood Neshati
|In this study, we aim to move forward the understanding of queries by identifying their related entity attributes from a knowledge base.
|Ready for Use: Subject-Independent Movement Intention Recognition via a Convolutional Attention Model
|Dalin Zhang, Lina Yao, Kaixuan Chen, Sen Wang
|In order to fill the gap, we present a Convolutional Attention Model (CAM) for EEG-based human movement intention recognition in the subject-independent scenario.
|Recommender Systems with Characterized Social Regularization
|Tzu-Heng Lin, Chen Gao, Yong Li
|In this paper, we present a novel CSR (short for C haracterized S ocial R egularization) model by designing a universal regularization term for modeling variable social influence.
|Recommending Serendipitous Items using Transfer Learning
|Gaurav Pandey, Denis Kotkov, Alexander Semenov
|Therefore, in the absence of any known deep learning algorithms for recommending serendipitous items and the lack of large serendipity oriented datasets, we introduce SerRec our novel transfer learning method to recommend serendipitous items.
|A Recurrent Neural Network for Sentiment Quantification
|Andrea Esuli, Alejandro Moreo Fern�ndez, Fabrizio Sebastiani
|We propose a recurrent neural network architecture for quantification (that we call QuaNet) that observes the classification predictions to learn higher-order “quantification embeddings”, which are then refined by incorporating quantification predictions of simple classify-and-count-like methods.
|Re-evaluating Embedding-Based Knowledge Graph Completion Methods
|Farahnaz Akrami, Lingbing Guo, Wei Hu, Chengkai Li
|Incompleteness of large knowledge graphs (KG) has motivated many researchers to propose methods to automatically find missing edges in KGs.
|Re-ranking Web Search Results for Better Fact-Checking: A Preliminary Study
|Khaled Yasser, Mucahid Kutlu, Tamer Elsayed
|To evaluate our proposed method, we conducted a preliminary study for which we have developed a test collection that includes 22 claims and 20 manually-annotated Web search results for each. In this paper, we introduce a new research problem that addresses the ability of fact-checking systems to distinguish Web search results that are useful in discovering the veracity of claims from the ones that are not.We also propose a re-ranking method to improve ranking of search results for fact-checking.
|Sci-Blogger: A Step Towards Automated Science Journalism
|Raghuram Vadapalli, Bakhtiyar Syed, Nishant Prabhu, Balaji Vasan Srinivasan, Vasudeva Varma
|In this work, we introduce the problem of automating some parts of the science journalism workflow by automatically generating the ‘title’ of a blog version of a scientific paper. We have built a corpus of $87,328$ pairs of research papers and their corresponding blogs from two science news aggregators and have used it to buildSci ence-Blogger – a pipeline-based architecture consisting of a two-stage mechanism to generate the blog titles.
|Semi-Supervised Collaborative Learning for Social Spammer and Spam Message Detection in Microblogging
|Fangzhao Wu, Chuhan Wu, Junxin Liu
|In this paper, we propose a semi-supervised collaborative learning approach to jointly detect social spammers and spam messages in microblogging platforms.
|A Sequential Neural Information Diffusion Model with Structure Attention
|Zhitao Wang, Chengyao Chen, Wenjie LI
|In this paper, we propose a novel sequential neural network with structure attention to model information diffusion.
|A Supervised Learning Framework for Prediction of Incompatible Herb Pair in Traditional Chinese Medicine
|Jiajing Zhu, Yongguo Liu, Shangming Yang, Shuangqing Zhai, Yi Zhang, Chuanbiao Wen
|In this paper, we propose a novel supervised learning framework for potential IHP prediction.
|TED-KISS: A Known-Item Speech Video Search Benchmark
|Fan Fang, Bo-Wen Zhang, Xu-Cheng Yin, Hai-Xia Man, Fang Zhou
|In order to embrace the research of known-item search, we present a new publicly available known-item speech video search benchmark, namely TED-KISS, which takes TED talks as an example.
|TEQUILA: Temporal Question Answering over Knowledge Bases
|Zhen Jia, Abdalghani Abujabal, Rishiraj Saha Roy, Jannik Str�tgen, Gerhard Weikum
|We present TEQUILA, an enabler method for temporal QA that can run on top of any KB-QA engine.
|Toward Automated Multiparty Privacy Conflict Detection
|Haoti Zhong, Anna Squicciarini, David Miller
|In an effort to support users’ decision making process in regards to shared and co-managed online images, in this paper we present a novel model to early detect images which may be subject to possible conflicting access control decisions.
|Towards a Quantum-Inspired Framework for Binary Classification
|Prayag Tiwari, Massimo Melucci
|In this paper, we address a specific task of ML and present a binary classification model inspired by the quantum detection framework.
|Towards Explainable Networked Prediction
|Liangyue Li, Hanghang Tong, Huan Liu
|Thus, we propose a multi-aspect, multi-level approach to explain networked prediction.
|Towards Partition-Aware Lifted Inference
|Melisachew Wudage Chekol, Heiner Stuckenschmidt
|In line with this, we propose a novel technique to automatically partition rules based on their structure for efficient parallel grounding.
|Unsupervised Evaluation of Text Co-clustering Algorithms Using Neural Word Embeddings
|Fran�ois Role, Stanislas Morbieu, Mohamed Nadif
|In this paper, we therefore propose an evaluation scheme that accounts for the two-dimensional nature of co-clustering algorithms, thus allowing for a more precise evaluation of their performance.
|User Identification with Spatio-Temporal Awareness across Social Networks
|Xing Gao, Wenli Ji, Yongjun Li, Yao Deng, Wei Dong
|To tackle these problems, we propose a novel approach that consists of three parts.
|Using Word Embeddings for Information Retrieval: How Collection and Term Normalization Choices Affect Performance
|Dwaipayan Roy, Debasis Ganguly, Sumit Bhatia, Srikanta Bedathur, Mandar Mitra
|We present quantitative estimates of similarity of word vectors obtained under different settings, and use embeddings based query expansion task to understand the effects of these parameters on IR effectiveness.
|Variational Recurrent Model for Session-based Recommendation
|Zhitao Wang, Chengyao Chen, Ke Zhang, Yu Lei, Wenjie LI
|In this paper, we propose a novel Variational Recurrent Model (VRM), which employs the stochastic latent variable to capture the knowledge of frequent click patterns and impose variability for the sequential behavior modeling.
|vec2Link: Unifying Heterogeneous Data for Social Link Prediction
|Fan Zhou, Bangying Wu, Yi Yang, Goce Trajcevski, Kunpeng Zhang, Ting Zhong
|In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations.
|W2E: A Worldwide-Event Benchmark Dataset for Topic Detection and Tracking
|Tuan-Anh Hoang, Khoi Duy Vo, Wolfgang Nejdl
|In this work, we address this issue by collecting and publishing W2E – a large dataset consisting of news articles from more than 50 prominent mass media channels worldwide.
|Weakly-Supervised Generative Adversarial Nets with Auxiliary Information for Wireless Coverage Estimation
|Zhuo Li, Hongwei Wang, Miao Zhao
|In this paper, we aim to estimate the wireless coverage of an area based on the randomly distributed samples of received signal strength collected within the area.
|Weave&Rec: A Word Embedding based 3-D Convolutional Network for News Recommendation
|Dhruv Khattar, Vaibhav Kumar, Vasudeva Varma, Manish Gupta
|In this paper we propose a novel deep learning model for news recommendation which utilizes the content of the news articles as well as the sequence in which the articles were read by the user.
|Word-Driven and Context-Aware Review Modeling for Recommendation
|Qianqian Wang, Si Li, Guang Chen
|Recently, convolutional neural networks(CNNs) has been demonstrated to effectively model reviews in recommender systems, due to the learning of contextual features such as surrounding words and word order for reviews.