Learning dual preferences with non-negative matrix tri-factorization for Top-N recommender system
Author(s)
Xie, Haoran
Wang, Philips Fu Lee
Author(s)
Li, X.
Rao, Y.
Chen, Y.
Lau, R. Y. K.
Yin, J.
Date Issued
2018
Publisher
Springer
Related Publication(s)
Database Systems for Advanced Applications (23rd International Conference, DASFAA 2018) Proceedings, Part I
Start page
133
End page
149
Abstract
In recommender systems, personal characteristic is possessed by not only users but also displaying products. Users have their personal rating patterns while products have different characteristics that attract users. This information can be explicitly exploited from the review text. However, most existing methods only model the review text as a topic preference of products, without considering the perspectives of users and products simultaneously. In this paper, we propose a user-product topic model to capture both user preferences and attractive characteristics of products. Different from conventional collaborative filtering in conjunction with topic models, we use non-negative matrix tri-factorization to jointly reveal the characteristic of users and products. Experiments on two real-world data sets validate the effectiveness of our method in Top-N recommendations.
SFU Affiliated Publication
Yes
Availability at SFU Library
No database links found.