Pharos: Social Map-Based Recommendation for Content-Centric Social Websites

Recommendation technologies are widely used in online social websites (e.g., forums and blogs) to help users locate their interests among overwhelming amounts of information. However, it is difficult to make effective recommendations for new users (a.k.a. the cold start problem) due to a lack of user information (e.g., preferences and interests). Furthermore, the complexity of recommendation algorithms may not be easily explained, leaving users with trust issues in recommendation results. To tackle the above two challenges, we are building Pharos, a social map-based recommender system. A social map summarizes users’ content-related social behavior (e.g., reading, writing, and commenting) over time as a set of latent communities. Each community describes the content being discussed and the people involved. Discovering, ranking, and recommending “popular” latent communities on a social map, Pharos enables new users to grasp the dynamics of a social website, alleviating the cold start problem. In addition, the map can also be used as a context for making and explaining recommendations about people and content. We have deployed Pharos internally and the preliminary evaluation shows the usefulness of Pharos.