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›› 2015, Vol. 30 ›› Issue (4): 917-932.

• Special Section on Social Media Processing — Part 1 •

### When Factorization Meets Heterogeneous Latent Topics: An Interpretable Cross-Site Recommendation Framework

Xin Xin1*(辛欣), Member, CCF, ACM, IEEE, Chin-Yew Lin2(林钦佑), Member, ACM, IEEE, Xiao-Chi Wei1(魏骁驰), Member, CCF, ACM, He-Yan Huang1(黄河燕), Member, ACM

1. 1. Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China;
2. Microsoft Research Asia, Beijing 100080, China
• Received:2014-11-15 Revised:2015-03-20 Online:2015-07-05 Published:2015-07-05
• Contact: Xin Xin is currently an assistant professor of the School of Computer Science, Beijing Institute of Technology. E-mail:xxin@bit.edu.cn
• About author:Xin Xin is currently an assistant professor of the School of Computer Science, Beijing Institute of Technology. He received his B.S. and M.S. degrees from the Department of Computer Science and Technology, Tsinghua University, Beijing, in 2006 and 2008, respectively, and his Ph.D. degree in computer science from The Chinese University of Hong Kong in 2011. His research interests include data mining, machine learning, etc. He is a member of CCF, ACM, and IEEE.
• Supported by:

This work was supported by the National Basic Research 973 Program of China under Grant No. 2013CB329605, the National Natural Science Foundation of China under Grant Nos. 61300076 and 61375045, the Ph.D. Programs Foundation of Ministry of Education of China under Grant No. 20131101120035, and the Excellent Young Scholars Research Fund of Beijing Institute of Technology.

Data sparsity is a well-known challenge in applications of recommender systems. Previous work alleviate this problem by incorporating the information within the corresponding social media site. In this paper, we are going to solve this challenge by exploring the cross-site information. Specifically, we target at: 1) How to effectively and efficiently utilize cross-site ratings and content features to improve the recommendation performance? and 2) How to make the recommendation interpretable by utilizing the content features? We propose a joint model of matrix factorization and latent topic analysis as the recommendation framework. In this model, heterogeneous content features can be modeled by multiple kinds of latent topics, by which the feature dimensionality reduction is accurately conducted for improving recommendation performance. In addition, the combination of matrix factorization and latent topics makes the recommendation result interpretable from many aspects. Therefore, the above two issues are simultaneously solved. Through a real world dataset, where user behaviors in three social media sites are collected, we demonstrate that the proposed model is effective in improving the recommendation performance and interpreting the rationale of ratings.

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