Similarity measures in scientometric research: the Jaccard index versus Salton's cosine formula
Information Processing and Management: an International Journal
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Online Discussion Participation Prediction Using Non-negative Matrix Factorization
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Movies Recommendation Networks as Bipartite Graphs
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
Collaborative filtering recommender systems
The adaptive web
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Trend analysis model: trend consists of temporal words, topics, and timestamps
Proceedings of the fourth ACM international conference on Web search and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Recommender system provides users with personalized suggestions of product or information. Typically, recommender systems rely on a bipartite graph model to capture user interest. As an extension, some boosted methods analyze content information to further improve the quality of personalized recommendation. However, due to the prevalence of short and sparse messages in online social media, traditional content-boosted methods do not guarantee to capture user preference accurately especially for web contents. In this paper, we propose a novel graphical model to extract hidden topics from web contents, cluster web contents, and detect users' interests on each cluster. In addition, we introduce two reranking models which utilize the detected user interest to further boost the quality of personalized recommendation. Experiment results on a public dataset demonstrated the limitation of a traditional content-boosted approach, and also showed the validity of our proposed techniques.