Music Discovery with Social Networks

Current music recommender systems rely on techniques like collaborative ltering on user-provided information in order to generate relevant recommendations based upon users’ music collections or listening habits. In this paper, we examine whether better recommendations can be obtained by taking into account the music preferences of the user’s social contacts. We assume that music is naturally diused through the social network of its listeners, and that we can propagate automatic recommendations in the same way through the network. In order to test this statement, we developed a music recommender application called Starnet on a Social Networking Service. It generated recommendations based either on positive ratings of friends (social recommendations), positive ratings of others in the network (nonsocial recommendations), or not based on ratings (random recommendations). The user responses to each type of recommendation indicate that social recommendations are better than non-social recommendations, which are in turn better than random recommendations. Likewise, the discovery of novel and relevant music is more likely via social recommendations than non-social. Social shue recommendations enable people to discover music through a serendipitous process powered by human relationships and tastes, exploiting the user’s social network to share cultural experiences.