Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
A. Popescul, L. Ungar, D. Pennock, and S. Lawrence. Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, page 437--444. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (August 2001)
Abstract
Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not...
%0 Conference Paper
%1 popescul2001probabilistic
%A Popescul, Alexandrin
%A Ungar, Lyle H.
%A Pennock, David M.
%A Lawrence, Steve
%B Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence
%C San Francisco, CA, USA
%D 2001
%I Morgan Kaufmann Publishers Inc.
%K 3mode clustering collaborative content mode network probabilistic recommender sparse three
%P 437--444
%T Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
%U http://dl.acm.org/citation.cfm?id=2074022.2074076
%X Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not...
@inproceedings{popescul2001probabilistic,
abstract = {Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not...},
added-at = {2006-07-16T14:21:19.000+0200},
address = {San Francisco, CA, USA},
author = {Popescul, Alexandrin and Ungar, Lyle H. and Pennock, David M. and Lawrence, Steve},
biburl = {https://www.bibsonomy.org/bibtex/25ae7718c24f485b059f4db54acf71d5b/jaeschke},
booktitle = {Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence},
interhash = {429bcf0381d2b7b9ab95eea7d3a65776},
intrahash = {5ae7718c24f485b059f4db54acf71d5b},
keywords = {3mode clustering collaborative content mode network probabilistic recommender sparse three},
month = aug,
pages = {437--444},
publisher = {Morgan Kaufmann Publishers Inc.},
series = {UAI'01},
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments},
url = {http://dl.acm.org/citation.cfm?id=2074022.2074076},
year = 2001
}