]> BibSonomy publications for /bibtex/2ae7ce7b8d1a31e81f9aa8b8367039506/jaeschke http://www.bibsonomy.org/burst/bibtex/2ae7ce7b8d1a31e81f9aa8b8367039506/jaeschke BibSonomy BuRST Feed for /bibtex/2ae7ce7b8d1a31e81f9aa8b8367039506/jaeschke 2008-10-12T19:13:07+02:00 Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments - Popescul, Ungar, Pennock, Lawrence (ResearchIndex)http://www.bibsonomy.org/bibtex/2ae7ce7b8d1a31e81f9aa8b8367039506/jaeschke jaeschke 2006-07-16T14:21:19+02:00 3mode clustering mode network three todo
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
Alexandrin Popescul and Lyle Ungar and David Pennock and Steve Lawrence 17th Conference on Uncertainty in Artificial Intelligence 437--444 (2001)
to 3mode clustering mode network three todo on 2006-07-16 14:21:19 ]]> Seattle, Washington17th Conference on Uncertainty in Artificial IntelligenceAugust 2--5437--444Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments20013mode clustering mode network three todo 2006-07-16 14:21:19.0Recommender 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...