A joint framework for collaborative filtering and metric learning

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

Research output: Contribution to conferencePaper

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.
Original languageEnglish
Publication statusPublished - Dec 2016

Fingerprint

Collaborative filtering
Singular value decomposition
Experiments

Citation

Wong, T.-L., Lam, W., Xie, H., & Wang, F. L. (2016, December). A joint framework for collaborative filtering and metric learning. Paper presented at The 2016 Asian Information Retrieval Societies Conference (AIRS 2016), Tsinghua University, Beijing, China.

Keywords

  • Collaborative filtering
  • Metric learning
  • Mahalanobis distance