Social Network Collaborative Filtering

Abstract

This paper reports on a preliminary empirical study comparing methods for collaborative filtering (CF) using explicit data on consumers’ social networks. To our knowledge it is the first study to carefully evaluate the potential of explicit, publicly represented social networks for making product recommendations. Understanding social-network CF is important because traditional CF over a large consumer base is tremendously expensive computationally. An often-ignored aspect of CF is the selection of the set of users from which to make recommendations. Social theory tells us that social relationships are likely to connect similar people. If this similarity is in line with the recommendation task, they may provide a small, dense set of “recommenders” for CF. We examine a unique dataset from Amazon.com that contains a social network of consumer-selected friends. We examine two ways to incorporate social-network information into CF: using the social network to restrict the set of recommenders selected, and (further) using proximity in the social network to modify the traditional CF calculation. The results show that that CF with social-network members selected as recommenders can be remarkably superior as compared to collaborative filtering with the recommenders not socially connected. Once the social network is selected, social network proximity does not seem to improve recommendations.