@inproceedings{popescul01probabilistic, 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...}, address = {Seattle, Washington}, author = {Popescul, Alexandrin and Ungar, Lyle and Pennock, David and Lawrence, Steve}, booktitle = {17th Conference on Uncertainty in Artificial Intelligence}, interhash = {429bcf0381d2b7b9ab95eea7d3a65776}, intrahash = {ae7ce7b8d1a31e81f9aa8b8367039506}, month = {August 2--5}, pages = {437--444}, title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments}, url = {http://citeseer.ist.psu.edu/popescul01probabilistic.html}, year = 2001 }