On Measuring Social Friend Interest Similarities In Recommender Systems

Social recommender system has become an emerging research topic due to the prevalence of online social networking services during the past few years. In this paper, aiming at providing fundamental support to the research of social recommendation problem, we conduct an in-depth analysis on the correlations between social friend relations and user interest similarities. When evaluating interest similarities without distinguishing different friends a user has, we surprisingly observe that social friend relations generally cannot represent user interest similarities. A user's average similarity on all his/her friends is even correlated with the average similarity on some other randomly selected users. However, when measuring interest similarities using a finer granularity, we find that the similarities between a user and his/her friends are actually controlled by the network structure in the friend network. Factors that affect the interest similarities include subgraph topology, connected components, number of co-friends, etc. We believe our analysis provides substantial impact for social recommendation research and will benefit ongoing research in both recommender systems and other social applications.