Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
A. Popescul, L. Ungar, D. Pennock, und S. Lawrence. 17th Conference on Uncertainty in Artificial Intelligence, Seite 437--444. Seattle, Washington, (August 2001)
Zusammenfassung
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...
Beschreibung
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments - Popescul, Ungar, Pennock, Lawrence (ResearchIndex)
