Content-boosted Collaborative Filtering for Improved Recommendations
P. Melville, R. Mooney, and R. Nagarajan. Eighteenth National Conference on Artificial Intelligence, page 187--192. Menlo Park, CA, USA, American Association for Artificial Intelligence, (2002)
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 Conference Paper
%1 melville2002contentboosted
%A Melville, Prem
%A Mooney, Raymod J.
%A Nagarajan, Ramadass
%B Eighteenth National Conference on Artificial Intelligence
%C Menlo Park, CA, USA
%D 2002
%I American Association for Artificial Intelligence
%K collaborative content filtering hybrid recommender
%P 187--192
%T Content-boosted Collaborative Filtering for Improved Recommendations
%U http://dl.acm.org/citation.cfm?id=777092.777124
%X 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.
%@ 0-262-51129-0
@inproceedings{melville2002contentboosted,
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.},
acmid = {777124},
added-at = {2015-02-16T17:25:55.000+0100},
address = {Menlo Park, CA, USA},
author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass},
biburl = {https://www.bibsonomy.org/bibtex/2a4917f0299f48e403966a8003ebd50be/hotho},
booktitle = {Eighteenth National Conference on Artificial Intelligence},
interhash = {985028099c1a29f116ad7434005895ac},
intrahash = {a4917f0299f48e403966a8003ebd50be},
isbn = {0-262-51129-0},
keywords = {collaborative content filtering hybrid recommender},
location = {Edmonton, Alberta, Canada},
numpages = {6},
pages = {187--192},
publisher = {American Association for Artificial Intelligence},
timestamp = {2015-02-16T17:25:55.000+0100},
title = {Content-boosted Collaborative Filtering for Improved Recommendations},
url = {http://dl.acm.org/citation.cfm?id=777092.777124},
year = 2002
}