A joint framework for collaborative and content filtering

Justin Basilico, Thomas Hofmann
2004 Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR '04  
This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. The framework incorporates all available information by coupling together multiple learning problems and using a suitable kernel or similarity function between user-item pairs. We propose and evaluate an on-line algorithm (JRank) that generalizes perceptron learning using this framework and shows significant improvement over other approaches.
doi:10.1145/1008992.1009115 dblp:conf/sigir/BasilicoH04 fatcat:4a7kvfj3czdy5obbxcbjnbbt4m