Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

TL;DR

This paper introduces a novel model that combines ratings, item reviews, social relations, and implicit feedback to improve personalized recommendations, demonstrating superior accuracy over existing methods.

Contribution

The paper proposes the MR3 model that effectively fuses ratings, reviews, and social data, and incorporates implicit feedback for enhanced recommendation performance.

Findings

Achieves more accurate rating predictions on real datasets.

Effectively combines multiple data sources for improved recommendations.

Demonstrates the impact of each data source and implicit feedback.

Abstract

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although {\em social matrix factorization} (Social MF) and {\em topic matrix factorization} (Topic MF) successfully exploit social relations and item reviews, respectively, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model {\em \mbox{MR3}} to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.