A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
This work addresses the challenge of data fusion in recommender systems for users and platforms, but it is incremental as it builds on existing matrix factorization methods.
The paper tackles the problem of improving rating prediction in recommender systems by combining ratings, social relations, and reviews, achieving more accurate predictions on two real-life datasets.
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.