Unifying Colloborative and Content-Based Filtering.
2004
Conference Paper
ei
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
|
BibTex @inproceedings{2739, 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}, doi = {} } |