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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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

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.

Keywords

Top-N recommender system Topic model Matrix tri-factorization 

Notes

Acknowledgements

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.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xiangsheng Li
    • 1
  • Yanghui Rao
    • 1
  • Haoran Xie
    • 2
  • Yufu Chen
    • 1
  • Raymond Y. K. Lau
    • 3
  • Fu Lee Wang
    • 4
  • Jian Yin
    • 5
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Department of Mathematics and Information TechnologyThe Education University of Hong KongTai PoHong Kong
  3. 3.Department of Information SystemsCity University of Hong KongKowloon TongHong Kong
  4. 4.Caritas Institute of Higher EducationTseung Kwan OHong Kong
  5. 5.Guangdong Key Laboratory of Big Data Analysis and ProcessingGuangzhouPeople’s Republic of China

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