Unifying Collaborative and Content-Based Filtering
Abstract
Collaborative and content-based filtering are two paradigms that have beenapplied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integratesall available training information such as past user-item ratings as well asattributes of items or users to learn a prediction function. The keyingredient of our method is the design of a suitable kernel or similarityfunction between user-item pairs that allows simultaneous generalizationacross the user and item dimensions. We propose an on-line algorithm (JRank)that generalizes perceptron learning. Experimental results on the EachMoviedata set show significant improvements over standard approaches.
Cite
Text
Basilico and Hofmann. "Unifying Collaborative and Content-Based Filtering." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015394Markdown
[Basilico and Hofmann. "Unifying Collaborative and Content-Based Filtering." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/basilico2004icml-unifying/) doi:10.1145/1015330.1015394BibTeX
@inproceedings{basilico2004icml-unifying,
title = {{Unifying Collaborative and Content-Based Filtering}},
author = {Basilico, Justin and Hofmann, Thomas},
booktitle = {International Conference on Machine Learning},
year = {2004},
doi = {10.1145/1015330.1015394},
url = {https://mlanthology.org/icml/2004/basilico2004icml-unifying/}
}