Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
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A Web Recommender System for Recommending, Predicting and Personalizing Music Playlists
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Pessimists and optimists: Improving collaborative filtering through sentiment analysis
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Personalized news recommendation via implicit social experts
Information Sciences: an International Journal
Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm
Information Systems Frontiers
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We propose a hybridization of collaborative filtering and content based recommendation system. Attributes used for content based recommendations are assigned weights depending on their importance to users. The weight values are estimated from a set of linear regression equations obtained from a social network graph which captures human judgment about similarity of items.