The Journal of Machine Learning Research
An architecture for parallel topic models
Proceedings of the VLDB Endowment
Democrats, republicans and starbucks afficionados: user classification in twitter
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
An analysis of topical proximity in the twitter social graph
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Exploring generative models of tripartite graphs for recommendation in social media
Proceedings of the 4th International Workshop on Modeling Social Media
Incorporating popularity in topic models for social network analysis
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Predicting purchase behaviors from social media
Proceedings of the 22nd international conference on World Wide Web
FRec: a novel framework of recommending users and communities in social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Recommending branded products from social media
Proceedings of the 7th ACM conference on Recommender systems
Social Link Prediction in Online Social Tagging Systems
ACM Transactions on Information Systems (TOIS)
Personalized recommendation based on review topics
Service Oriented Computing and Applications
Identifying interesting Twitter contents using topical analysis
Expert Systems with Applications: An International Journal
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This paper presents a user recommendation system that recommends to a user new friends having similar interests. We automatically discover users' interests using Latent Dirichlet Allocation (LDA), a linguistic topic model that represents users as mixtures of topics. Our system is able to recommend friends for 4 million users with high recall, outperforming existing strategies based on graph analysis.