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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.
@inproceedings{1009115, author = {Justin Basilico and Thomas Hofmann}, title = {A joint framework for collaborative and content filtering}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {550--551}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009115}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
