A Survey of Social Recommender System

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items might be of her interest. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of social recommender systems. We provide a brief overview over the task of recommender systems and the traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify social recommender systems into two categories: social-network traversal-based approaches and social collaborative-filtering based approaches. For each category, we survey and compare several representative algorithms.