Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An Introduction to Variational Methods for Graphical Models
Machine Learning
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Internet Technology (TOIT)
An MDP-Based Recommender System
The Journal of Machine Learning Research
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
On ranking controversies in wikipedia: models and evaluation
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Topic-link LDA: joint models of topic and author community
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A generalized Co-HITS algorithm and its application to bipartite graphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Privacy in Social Networks: How Risky is Your Social Graph?
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Friend or frenemy?: predicting signed ties in social networks
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th Annual ACM Symposium on Applied Computing
DIGTOBI: a recommendation system for Digg articles using probabilistic modeling
Proceedings of the 22nd international conference on World Wide Web
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Twitter provides search services to help people find users to follow by recommending popular users or the friends of their friends. However, these services neither offer the most relevant users to follow nor provide a way to find the most interesting tweet messages for each user. Recently, collaborative filtering techniques for recommendations based on friend relationships in social networks have been widely investigated. However, since such techniques do not work well when friend relationships are not sufficient, we need to take advantage of as much other information as possible to improve the performance of recommendations. In this paper, we propose TWILITE, a recommendation system for Twitter using probabilistic modeling based on latent Dirichlet allocation which recommends top-K users to follow and top-K tweets to read for a user. Our model can capture the realistic process of posting tweet messages by generalizing an LDA model as well as the process of connecting to friends by utilizing matrix factorization. We next develop an inference algorithm based on the variational EM algorithm for learning model parameters. Based on the estimated model parameters, we also present effective personalized recommendation algorithms to find the users to follow as well as the interesting tweet messages to read. The performance study with real-life data sets confirms the effectiveness of the proposed model and the accuracy of our personalized recommendations.