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...
Описание
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments - Popescul, Ungar, Pennock, Lawrence (ResearchIndex)
%0 Conference Paper
%1 popescul01probabilistic
%A Popescul, Alexandrin
%A Ungar, Lyle
%A Pennock, David
%A Lawrence, Steve
%B 17th Conference on Uncertainty in Artificial Intelligence
%C Seattle, Washington
%D 2001
%K clustering recommender plsi 3mode
%P 437--444
%T Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
%U http://citeseer.ist.psu.edu/popescul01probabilistic.html
%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{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...},
added-at = {2006-09-20T17:36:01.000+0200},
address = {Seattle, Washington},
author = {Popescul, Alexandrin and Ungar, Lyle and Pennock, David and Lawrence, Steve},
biburl = {https://www.bibsonomy.org/bibtex/2ae7ce7b8d1a31e81f9aa8b8367039506/hotho},
booktitle = {17th Conference on Uncertainty in Artificial Intelligence},
description = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments - Popescul, Ungar, Pennock, Lawrence (ResearchIndex)},
interhash = {429bcf0381d2b7b9ab95eea7d3a65776},
intrahash = {ae7ce7b8d1a31e81f9aa8b8367039506},
keywords = {clustering recommender plsi 3mode},
month = {August 2--5},
pages = {437--444},
timestamp = {2006-09-20T17:36:01.000+0200},
title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments},
url = {http://citeseer.ist.psu.edu/popescul01probabilistic.html},
year = 2001
}