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Abstract: Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that... (Update)
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BibTeX entry: (Update)
J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering. In Proceedings of ICML'04, Twenty-first International Conference on Machine Learning. ACM Press, New York, 2004. http://citeseer.ist.psu.edu/basilico04unifying.html More
@misc{ basilico04unifying,
author = "J. Basilico and T. Hofmann",
title = "Unifying collaborative and content-based filtering",
text = "J. Basilico and T. Hofmann. Unifying collaborative and content-based filtering.
In Proceedings of ICML'04, Twenty-first International Conference on Machine
Learning. ACM Press, New York, 2004.",
year = "2004",
url = "citeseer.ist.psu.edu/basilico04unifying.html" }
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