Recommendations in taste related domains: Collaborative filtering vs. social filtering

We investigate how social networks can be used in recommendation generation in taste related domains. Social Filtering (using social networks for neighborhood generation) is compared to Collaborative Filtering with respect to prediction accuracy in the domain of rating clubs. After reviewing background and related work, we present an extensive empirical study where over thousand participants from a social networking community where asked to provide ratings for clubs in Munich. We then compare a typical traditional CF-approach to a social recommender / social filtering approach where friends from the underlying social network are used as rating neighborhood and analyze the experiments statistically. Surprisingly, the social filtering approach outperforms the CF approach in all variants of the experiment. The implications of the experiment for professional and private-life collaborative environments and services where recommendations play a role are discussed. We conclude with future perspectives on social recommender systems, especially in upcoming mobile environments.