Recommender systems attempt to find relevant data for their users. As the body of data available in the Web sphere becomes larger, this task becomes increasingly harder. In this paper we present a comparison of recommendation results when using different social and pseudo-social features commonly available in online movie recommendation communities. Social relations, whether inferred or not, hold implicit information about users’ taste and interests. We present results of a simple method that extends standard collaborative filtering algorithms to include a social network and show that this explicit and implicit information (i.e. direct friendship, and indirect co-commenting etc.) can be used to improve the quality of recommendations.