Cross-domain collaborative filtering over time

Authors:
Bin Li;Xingquan Zhu;Ruijiang Li;Chengqi Zhang;Xiangyang Xue;Xindong Wu
Affiliations:
QCIS Centre, FEIT, University of Technology, Sydney, NSW, Australia;QCIS Centre, FEIT, University of Technology, Sydney, NSW, Australia;School of Computer Science, Fudan University, Shanghai, China;QCIS Centre, FEIT, University of Technology, Sydney, NSW, Australia;School of Computer Science, Fudan University, Shanghai, China;Department of Computer Science, Hefei University of Technology, Hefei, China and Department of Computer Science, University of Vermont, Burlington, VT
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Year:
2011

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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. However, 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 multiple counterparts over temporal domains and successive 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 recommendation performance as well as explicitly track and visualize user-interest drift over time.