Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments

by: Alexandrin Popescul, Lyle Ungar, David Pennock, Steve Lawrence

(August February--May 2001), pp. 437-444.

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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...

@inproceedings{citeulike:838786, 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}, citeulike-article-id = {838786}, keywords = {collaborative\_filtering, mixture\_models}, month = {August February--May}, pages = {437--444}, posted-at = {2006-09-17 19:02:06}, priority = {0}, title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments}, url = {http://citeseer.ist.psu.edu/popescul01probabilistic.html}, year = {2001} }

RIS record

TY - CONF ID - citeulike:838786 L3 - citeulike-article-id:838786 TI - Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments T2 - 17th Conference on Uncertainty in Artificial Intelligence SP - 437 EP - 444 AD - Seattle, Washington N2 - 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... KW - collaborative_filtering KW - mixture_models AU - Popescul, Alexandrin AU - Ungar, Lyle AU - Pennock, David AU - Lawrence, Steve PY - 2001/August 2--5// UR - http://citeseer.ist.psu.edu/popescul01probabilistic.html ER -

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