{"type":"ScholarlyArticle","title":"Cross-Domain Collaborative Filtering over Time","authors":[{"name":"Bin Li","slug":"bin-li","given":"Bin","family":"Li","affiliation":""},{"name":"Xingquan Zhu","slug":"xingquan-zhu","given":"Xingquan","family":"Zhu","affiliation":""},{"name":"Ruijiang Li","slug":"ruijiang-li","given":"Ruijiang","family":"Li","affiliation":""},{"name":"Chengqi Zhang","slug":"chengqi-zhang","given":"Chengqi","family":"Zhang","affiliation":""},{"name":"Xiangyang Xue","slug":"xiangyang-xue","given":"Xiangyang","family":"Xue","affiliation":""},{"name":"Xindong Wu","slug":"xindong-wu","given":"Xindong","family":"Wu","affiliation":""}],"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.","doi":"10.5591/978-1-57735-516-8/IJCAI11-382","url":"https://ijcai.org/proceedings/2011/382","pdf":"https://ijcai.org/proceedings/2011/0382.pdf","markdown":"https://ijcai.org/proceedings/2011/382.md","bibtex":"https://ijcai.org/proceedings/2011/bibtex/382","volume":{"slug":"2011","title":"Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2011","url":"https://ijcai.org/proceedings/2011/","year":2011,"location":"Barcelona, Catalonia, Spain","dates":"16–22 July 2011","editors":"Toby Walsh","isbn":"978-1-57735-512-0; 978-1-57735-513-7; 978-1-57735-514-4; 978-1-57735-515-1; 978-1-57735-516-8","publisher":"AAAI Press/International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":382,"pages":{"first":2293,"last":2297},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}