Design Considerations for a Social Network-Based Recommendation System (SNRS)

Abstract

The effects of homophily among friends have demonstrated their importance to product marketing. However, it has rarely been considered in recommender systems. In this chapter, we propose a new paradigm of recommender systems which can significantly improve performance by utilizing information in social networks including user preference, item likability, and homophily. A probabilistic model, named SNRS, is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results from this dataset show that SNRS not only improves the prediction accuracy of recommender systems, but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of SNRS by applying semantic filtering of social networks and validate its improvement via a class project experiment. In this experiment, we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most content providers. Finally, we discuss two trust issues in recommender systems and show how SNRS can be extended to solve these problems.