Recommendation for Repeat Consumption from User Implicit Feedback

Jun Chen, Chaokun Wang, Jianmin Wang, Philip S. Yu

Abstract: Most of the previous work on recommender systems focuses on discovering novel items that meet users' personalized interest. But there is barely any study about recommending repeat items that consumed by the target user before. In fact, people's consumption behaviors are a mixture of repeat and novelty-seeking behaviors [1]. Since people forget about things as time elapses, it is possible that users may prefer the previously consumed items but cannot remember them at certain times. Therefore, Recommendation for Repeat Consumption (RRC) has some real utility and should be studied in depth. Some efforts have been done in related work [1]. However, they only consider item popularity and recency effect, and fail to take full advantage of behavioral features. In this paper, we attempt to address the RRC problem (illustrated in Fig 1) by proposing a Time-Sensitive Personalized Pairwise Ranking (abbr. TS-PPR) model based on the behavioral features extracted from user implicit feedback in the consumption history. TS-PPR factorizes the temporal useritem interactions via learning the mappings from the behavioral features in observable space to the preference features in latent space, and combines users static and dynamic preferences together in recommendation. An empirical study on real-world data sets shows encouraging results.