A joint framework for collaborative filtering and metric learning

Tak-Lam WONG, Wai LAM, Haoran XIE, Fu Lee WANG

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearch

1 Citation (Scopus)


We have developed a framework for jointly conducting collaborative filtering and distance metric learning based on regularized singular value decomposition (RSVD), which discovers the user matrix and item matrix in the low rank space. Our approach is able to solve RSVD and simultaneously learn the parameters of Mahalanobis distance considering the ratings given by similar users and dissimilar users. One characteristic of our approach is that the learned model can be effectively applied to rating prediction and other relevant applications such as trust prediction, resulting in a solution which is coherent and optimal to both tasks. Another characteristic is that social community information and similarity information can be easily considered in our framework. We have conducted extensive experiments on rating prediction using real-world datasets to evaluate our framework. We have also compared our framework with other existing works to illustrate the effectiveness. Experimental results show that our framework achieves a promising prediction performance and outperforms the existing works.
Original languageEnglish
Title of host publicationInformation Retrieval Technology : 12th Asia Information Retrieval Societies Conference, AIRS 2016, Beijing, China, November 30–December 2, 2016, proceedings
EditorsShaoping MA, Ji-Rong WEN, Yiqun LIU, Zhicheng DOU, Min ZHANG, Yi CHANG, Xin ZHAO
PublisherSpringer International Publishing AG
Number of pages13
ISBN (Electronic)9783319480510
ISBN (Print)9783319480503
Publication statusPublished - 2016
Externally publishedYes
Event12th Asia Information Retrieval Societies Conference - Tsinghua University, Beijing, China
Duration: 30 Nov 20162 Dec 2016

Publication series

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


Conference12th Asia Information Retrieval Societies Conference
Abbreviated titleAIRS 2016

Bibliographical note

The work described in this paper is substantially supported by grants from the Education University of Hong Kong (Project Codes: RG 30/2014-2015R and RG 18/2015-2016R).


  • Collaborative filtering
  • Metric learning
  • Mahalanobis distance


Dive into the research topics of 'A joint framework for collaborative filtering and metric learning'. Together they form a unique fingerprint.

Cite this