Learning Dual Preferences with Non-negative Matrix Tri-Factorization for Top-N Recommender System

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Author(s)

  • Xiangsheng Li
  • Yanghui Rao
  • Haoran Xie
  • Yufu Chen
  • Fu Lee Wang
  • Jian Yin

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings
EditorsJian Pei, Yannis Manolopoulos, Shazia Sadiq
PublisherSpringer, Cham
Pages133-149
ISBN (Electronic)9783319914527
ISBN (Print)9783319914510
Publication statusPublished - May 2018

Publication series

NameLecture Notes in Computer Science
Volume10827
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title23rd International Conference on Database Systems for Advanced Applications (DASFAA 2018)
PlaceAustralia
CityGold Coast
Period21 - 24 May 2018

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.

Research Area(s)

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

Citation Format(s)

Learning Dual Preferences with Non-negative Matrix Tri-Factorization for Top-N Recommender System. / Li, Xiangsheng; Rao, Yanghui; Xie, Haoran et al.
Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings. ed. / Jian Pei; Yannis Manolopoulos; Shazia Sadiq. Springer, Cham, 2018. p. 133-149 (Lecture Notes in Computer Science; Vol. 10827).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review