Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative Filtering Using Weighted Majority Prediction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Document preprocessing for naive Bayes classification and clustering with mixture of multinomials
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
User comments for news recommendation in forum-based social media
Information Sciences: an International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Online community is an important source for latest news and information. Accurate prediction of a user's interest can help provide better user experience. In this paper, we develop a recommendation system for online forums. There are a lot of differences between online forums and formal media. For example, content generated by users in online forums contains more noise compared to formal documents. Content topics in the same forum are more focused than sources like news websites. Some of these differences present challenges to traditional word-based user profiling and recommendation systems, but some also provide opportunities for better recommendation performance. In our recommendation system, we propose to (a) use latent topics to interpolate with content-based recommendation; (b) model latent user groups to utilize information from other users. We have collected three types of forum data sets. Our experimental results demonstrate that our proposed hybrid approach works well in all three types of forums.