A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2004; you can also visit the original URL.
The file type is
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