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

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.