Article

Unifying collaborative and content-based filtering

Authors:

Justin Basilico,

Thomas HofmannAuthors Info & Claims

ICML '04: Proceedings of the twenty-first international conference on Machine learning

Page 9

https://doi.org/10.1145/1015330.1015394

Published: 04 July 2004 Publication History

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.

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Published In

cover image ACM Other conferences

ICML '04: Proceedings of the twenty-first international conference on Machine learning

July 2004

934 pages

ISBN:1581138385

DOI:10.1145/1015330

Conference Chair:
Carla Brodley
Purdue University/Tufts University

Copyright © 2004 Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 July 2004

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https://doi.org/10.1109/TNNLS.2023.3329808
Cai WYang MLin L(2024)An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group PreferencesSystems10.3390/systems1211049112:11(491)Online publication date: 14-Nov-2024
https://doi.org/10.3390/systems12110491
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