MapReduce algorithms for big data analysis
Proceedings of the VLDB Endowment
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter
Proceedings of the sixth ACM international conference on Web search and data mining
User Features and Social Networks for Topic Modeling in Online Social Media
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Informational friend recommendation in social media
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Estimating sharer reputation via social data calibration
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
ProfileRank: finding relevant content and influential users based on information diffusion
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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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.