English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Unifying Colloborative and Content-Based Filtering.

MPS-Authors
/persons/resource/persons83975

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

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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
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.