Recommendation for Repeat Consumption from User Implicit Feedback

Recommender system has been studied as a useful tool to discover novel items for users while fitting their personalized interest. Thus, the previously consumed items are usually out of consideration due to the lack of novelty. However, as time elapses, people may forget those previously consumed and preferred items which could become novel again. Meanwhile, repeat consumption accounts for a major portion of people's observed activities; examples include: eating regularly at a same restaurant, or repeatedly listening to the same songs. Therefore, we believe that recommending repeat consumption will have a real utility at certain times. In this paper, we formulate the problem of recommendation for repeat consumption with user implicit feedback. A time-sensitive personalized pairwise ranking (TS-PPR) method based on user behavioral features is proposed to address this problem. The proposed method factorizes the temporal user-item 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.