Temporal Diversity In Recommender Systems

Abstract

Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how <i>accurately</i> they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items <i>over time</i>: the temporal characteristics of the system's top-<i>N</i> recommendations are not investigated. In particular, there is no means of measuring the extent that the <i>same items</i> are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the <i>diversity</i> in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy.