Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
SoRec: social recommendation using probabilistic matrix factorization
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
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Generalized Probabilistic Matrix Factorizations for Collaborative Filtering
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
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning personal + social latent factor model for social recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recently how to recommend celebrities to the public becomes an interesting problem on the social network websites, such as Twitter and Tencent Weibo. In this paper, we proposed a unified hierarchical Bayesian model to recommend celebrities to the general users. Specifically, we proposed to leverage both social network and descriptions of celebrities to improve the prediction ability and recommendation interpretability. In our model, we combine topic model with matrix factorization for both social network of celebrities and user following action matrix. It works by regularizing celebrity factors through celebrity's social network and descriptive words associated with each celebrity. We also proposed to incorporate different confidences for different dyadic contexts to handle the situation that only positive observations exist. We conducted experiments on two real-world datasets from Twitter and Tencent Weibo, which are the largest and second largest microblog websites in USA and China, respectively. The experiment results show that our model achieves a higher performance and provide more effective results than the state-of-art methods especially when recommending new celebrities. We also show that our model captures user intertests more precisely and gives better recommendation interpretability.