@inproceedings{2739,
  title = {Unifying Colloborative and Content-Based Filtering.},
  journal = {Proceedings of the 21st International Conference on Machine Learning},
  booktitle = {ACM International Conference Proceeding Series},
  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.},
  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},
  author = {Basilico, J. and Hofmann, T.}
}