Developing a User Recommendation Engine on Twitter Using Estimated Latent Topics

In recent years, microblogging is popular among people and informal communication becomes important in various communities. Therefore, a number of Web communication tools are developed to facilitate informal communication. In this paper, focusing on microblogging service, Twitter, we develop a user recommendation engine which extracts latent topics of users based on followings, lists, mentions and RTs. This recommendation algorithm is based on Latent Dirichlet Allocation (LDA) and KL divergence between two users' latent topics. This algorithm hypothesizes that the users have latent connection if the distance calculated by KL divergence is short. Additionally, we performed an experiment to evaluate the effectiveness of the algorithm, and this showed that there is correlation between the distance and user's preference obtained through questionnaire.