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Conference Paper

Unifying Colloborative and Content-Based Filtering.


Hofmann,  T
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Basilico, J., & Hofmann, T. (2004). Unifying Colloborative and Content-Based Filtering. Proceedings of the 21st International Conference on Machine Learning, 65.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F376-4
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 allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.