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Unifying Colloborative and Content-Based Filtering.


Conference Paper


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.

Author(s): Basilico, J. and Hofmann, T.
Book Title: ACM International Conference Proceeding Series
Journal: Proceedings of the 21st International Conference on Machine Learning
Pages: 65
Year: 2004
Day: 0
Editors: Greiner, R. , D. Schuurmans
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: ICLM 2004
Event Place: Banff, Alberta, Canada

Address: New York, USA
Digital: 0
Institution: Max-Planck for biological Cybernetics, Tübingen, Germany
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Unifying Colloborative and Content-Based Filtering.},
  author = {Basilico, J. and Hofmann, T.},
  journal = {Proceedings of the 21st International Conference on Machine Learning},
  booktitle = {ACM International Conference Proceeding Series},
  pages = {65 },
  editors = {Greiner, R. , D. Schuurmans},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck for biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  address = {New York, USA},
  year = {2004}