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

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Research

1 Citation (Scopus)


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.
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
PublisherSpringer International Publishing AG
Number of pages17
ISBN (Electronic)9783319914527
ISBN (Print)9783319914510
Publication statusPublished - 2018
Externally publishedYes
Event23rd International Conference on Database Systems for Advanced Applications - Gold Coast, Australia
Duration: 21 May 201824 May 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Database Systems for Advanced Applications
Abbreviated titleDASFAA 2018
Internet address

Bibliographical note

We are grateful to the anonymous reviewers for their valuable comments on this manuscript. The research has been supported by the National Natural Science Foundation of China (61502545, U1611264, U1711262), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), and the Individual Research Scheme of the Dean’s Research Fund 2017–2018 (FLASS/DRF/IRS-8) of The Education University of Hong Kong.


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


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