Learning dual preferences with non-negative matrix tri-factorization for Top-N recommender system

Xiangsheng LI, Yanghui RAO, Haoran XIE, Yufu CHEN, Raymond Y. K. LAU, Fu Lee WANG, Jian YIN

Research output: Chapter in Book/Report/Conference proceedingChapters

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. Copyright © 2018 Springer International Publishing AG, part of Springer Nature.
Original languageEnglish
Title of host publicationDatabase systems for advanced applications: 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21-24, 2018, Proceedings, Part I
EditorsJian PEI, Yannis MANOLOPOULOS, Shazia SADIQ, Jianxin LI
Place of PublicationCham
PublisherSpringer
Pages133-149
ISBN (Electronic)9783319914527
ISBN (Print)9783319914510
DOIs
Publication statusPublished - 2018

Citation

Li, X., Rao, Y., Xie, H., Chen, Y., Lau, R. Y. K., Wang, F. L., & Yin, J. (2018). Learning dual preferences with non-negative matrix tri-factorization for Top-N recommender system. In J. Pei, Y. Manolopoulos, S. Sadiq, & J. Li (Eds.), Database systems for advanced applications: 23rd International Conference, DASFAA 2018, Gold Coast, QLD, Australia, May 21-24, 2018, Proceedings, Part I (pp. 133-149). Cham: Springer.

Keywords

  • Top-N recommender system
  • Topic model
  • Matrix tri-factorization

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