Unifying collaborative and content-based filtering

  • Authors:
  • Justin Basilico;Thomas Hofmann

  • Affiliations:
  • Brown University, Providence, RI;Brown University, Providence, RI

  • Venue:
  • ICML '04 Proceedings of the twenty-first international conference on Machine learning
  • Year:
  • 2004

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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 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.