Melville, P.; Mooney, R. J. & Nagarajan, R.
(2002):
Content-boosted Collaborative Filtering for Improved Recommendations.
In: Eighteenth National Conference on Artificial Intelligence,
Menlo Park, CA, USA.
[Volltext]
[Kurzfassung] [BibTeX][Endnote]
Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.
@inproceedings{melville2002contentboosted,
author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass},
title = {Content-boosted Collaborative Filtering for Improved Recommendations},
booktitle = {Eighteenth National Conference on Artificial Intelligence},
publisher = {American Association for Artificial Intelligence},
address = {Menlo Park, CA, USA},
year = {2002},
pages = {187--192},
url = {http://dl.acm.org/citation.cfm?id=777092.777124},
isbn = {0-262-51129-0},
keywords = {hybrid, recommender, collaborative, filtering, content},
abstract = {Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.}
}
%0 = inproceedings
%A = Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass
%B = Eighteenth National Conference on Artificial Intelligence
%C = Menlo Park, CA, USA
%D = 2002
%I = American Association for Artificial Intelligence
%T = Content-boosted Collaborative Filtering for Improved Recommendations
%U = http://dl.acm.org/citation.cfm?id=777092.777124