Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation

Authors

  • Li Gao Chinese Academy of Sciences
  • Jia Wu University of Technology Sydney
  • Chuan Zhou Chinese Academy of Sciences
  • Yue Hu Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v31i1.10726

Keywords:

recommender system, dynamic sparse topic modeling, user profile evolution

Abstract

In many time-aware item recommender systems, modeling the accurate evolution of both user profiles and the contents of items over time is essential. However, most existing methods focus on learning users' dynamic interests, where the contents of items are assumed to be stable over time. They thus fail to capture the dynamic changes in the item's contents. In this paper, we present a novel method CDUE for time-aware item recommendation, which captures the evolution of both user's interests and item's contents information via topic dynamics. Specifically, we propose a dynamic sparse topic model to track the evolution of topics for changes in items' contents over time and adapt a vector autoregressive model to profile users' dynamic interests. The item's topics and user's interests and their evolutions are learned collaboratively and simultaneously into a unified learning framework. Experimental results on two real-world data sets demonstrate the quality and effectiveness of the proposed method and show that our method can be used to make better future recommendations.

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Published

2017-02-12

How to Cite

Gao, L., Wu, J., Zhou, C., & Hu, Y. (2017). Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10726

Issue

Section

Main Track: Machine Learning Applications