A joint framework for collaborative and content filtering

ABSTRACT

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.

REFERENCES

• 1: Chumki Basu , Haym Hirsh , William Cohen, Recommendation as classification: using social and content-based information in recommendation, Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, p.714-720, July 1998, Madison, Wisconsin, United States
• 2: J. S. Breese, D. Heckerman, and C. Kardie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 43--52, 1998.
• 3: K. Crammer and Y. Singer. Pranking with ranking. In Advances in Neural Information Processing Systems 14, pages 641--647, 2002.
• 4: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Algorithms, Experimentation