Tracking user-preference varying speed in collaborative filtering

Publication Type:
Conference Proceeding
Proceedings of the National Conference on Artificial Intelligence, 2011, 1 pp. 133 - 138
Issue Date:
Filename Description Size
Thumbnail2010004926OK.pdf954.81 kB
Adobe PDF
Full metadata record
In real-world recommender systems, some users are easily influenced by new products and whereas others are unwilling to change their minds. So the preference varying speeds for users are different. Based on this observation, we propose a dynamic nonlinear matrix factorization model for collaborative filtering, aimed to improve the rating prediction performance as well as track the preference varying speeds for different users. We assume that user-preference changes smoothly over time, and the preference varying speeds for users are different. These two assumptions are incorporated into the proposed model as prior knowledge on user feature vectors, which can be learned efficiently by MAP estimation. The experimental results show that our method not only achieves state-of-the-art performance in the rating prediction task, but also provides an effective way to track user-preference varying speed. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.
Please use this identifier to cite or link to this item: