The Journal of Machine Learning Research
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 18th international conference on World wide web
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
On the quality of inferring interests from social neighbors
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving user interest inference from social neighbors
Proceedings of the 20th ACM international conference on Information and knowledge management
Large-scale behavioral targeting with a social twist
Proceedings of the 20th ACM international conference on Information and knowledge management
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
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Uncovering user interest plays an important role to develop personalized systems in various fields including the Web and pervasive computing. In particular, online social networks (OSNs) are being spotlighted as the means to understand users' social behavior out of abundant online social information. In this paper, we explore a computational method of inferring user interest in Facebook by combining the degree of familiarity and topic similarity with social neighbors based on social correlation phenomenon. By conducting a question-naire survey, we demonstrate that our proposed method increases the accuracy of inference by 12.4% compared to existing methods which do not consider the latent topic structure implied in social contents.