Abstract

Collaborative filtering (CF) techniques recommend items to users based on their historical ratings. In real-world scenarios, user interests may drift over time since they are affected by moods, contexts,and pop culture trends. This leads to the fact that a user’s historical ratings comprise many aspects of user interests spanning a long time period. How-ever, at a certain time slice, one user’s interest may only focus on one or a couple of aspects. Thus,CF techniques based on the entire historical ratings may recommend inappropriate items. In this paper,we consider modeling user-interest drift over time based on the assumption that each user has mul-tiple counterparts over temporal domains and suc-cessive counterparts are closely related. We adopt the cross-domain CF framework to share the static group-level rating matrix across temporal domains,and let user-interest distribution over item groups drift slightly between successive temporal domains.The derived method is based on a Bayesian latent factor model which can be inferred using Gibbs sampling. Our experimental results show that our method can achieve state-of-the-art recommenda-tion performance as well as explicitly track and vi-sualize user-intere st drift over time.