A joint framework for collaborative filtering and metric learning

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

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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. Copyright © 2016 Springer International Publishing AG.
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
Place of PublicationCham
PublisherSpringer International Publishing AG
Pages184-196
ISBN (Print)9783319480503, 9783319480510
DOIs
Publication statusPublished - 2016

    Fingerprint

Citation

Wong, T.-L., Lam, W., Xie, H., & Wang, F. L. (2016). A joint framework for collaborative filtering and metric learning. In S. Ma, J.-R. Wen, Y. Liu, Z. Dou, M. Zhang, Y. Chang, et al. (Eds.), Information retrieval technology: 12th Asia Information Retrieval Societies Conference, AIRS 2016 Beijing, China, November 30 – December 2, 2016 proceedings (pp. 184-196). Cham: Springer International Publishing AG.

Keywords

  • Collaborative filtering
  • Metric learning
  • Mahalanobis distance