Social Recommendation With Characterized Regularization

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social networks. Existing social recommendation methods are based on the assumption, so-called social-trust, that users' preference or decision is influenced by their social-connected friends' purchase behaviors. However, they assume that the influences of social relationships are always the same, which violates the fact that users are likely to share preference on different products with different friends. More precisely, friends' behaviors do not necessarily affect a user's preferences, and the influence is diverse among different items. In this paper, we contribute a new solution, CSR (short for Characterized Social Regularization) model by designing a universal regularization term for modeling variable social influence. This regularization term captures the finely grained similarity of social-connected friends. We further introduce two variants of our model with different optimization manners. Our proposed model can be applied to both explicit and implicit interaction due to its high generality. Extensive experiments on three real-world datasets demonstrate that our CSR can outperform state-of-the-art social recommendation methods. Further experiments show that CSR can improve recommendation performance for those users with sparse social relations or behavioral interactions.

Automated summary

Social recommendation, although widely utilized in enhancing recommender systems, often assumes a uniform influence of social relationships, which is not reflective of the fact that users may have diverse preferences with different friends. In this paper, the CSR model is proposed to address this issue by introducing a universal regularization term that captures the varying social influence. Experimental results demonstrate the superiority of the CSR model over existing social recommendation methods, particularly for users with sparse social relations or behavioral interactions.