TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling
Younghoon Kim, Kyuseok Shim · 2011
Twitter provides search services to help people find new users to follow by recommending popular users or their friends' friends. However, these services do not offer the most relevant users to follow for a user. Furthermore, Twitter does not provide yet the search services to find the most interesting tweet messages for a user either. In this paper, we propose TWITOBI, a recommendation system for Twitter using probabilistic modeling for collaborative filtering which can recommend top-K users to follow and top-K tweets to read for a user. Our novel probabilistic model utilizes not only tweet messages but also the relationships between users. We develop an estimation algorithm for learning our model parameters and present its parallelized algorithm using MapReduce to handle large data. Our performance study with real-life data sets confirms the effectiveness and scalability of our algorithms.