Connecting Users with Similar Interests Across Multiple Web Services

Most online social networking services provide a feature for users to build interest groups. Based on the profiles and behavior data, web services can assist users to join groups by recommending relevant interest groups. In this paper, we propose a novel method to connect users across multiple services based on user-labeled tags. Tags represent interests of a user and have advantages in terms of privacy, up-to-datedness, and service coverage. We have collected tags from six popular web services, and analyzed usage patterns. We observe that the popularity of tags is highly skewed and dependent on the web services. We have also found that a set of tags of a single user frequently changes over time. Through user study, we demonstrate that the vector space model combined with intra-personomy normalization is good enough to recommend other users with similar interests.