---
title: "Cross-Domain Collaborative Filtering over Time"
authors:
  - "Bin Li"
  - "Xingquan Zhu"
  - "Ruijiang Li"
  - "Chengqi Zhang"
  - "Xiangyang Xue"
  - "Xindong Wu"
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"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "2293-2297"
track: ""
keywords:
---

# Cross-Domain Collaborative Filtering over Time

Bin Li, Xingquan Zhu, Ruijiang Li, Chengqi Zhang, Xiangyang Xue, Xindong Wu

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 2293-2297. DOI: [10.5591/978-1-57735-516-8/IJCAI11-382](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-382)

## 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.