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)

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...

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@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://puma.uni-kassel.de/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 = {2011-08-02T14:47:09.000+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
}

%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...

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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)

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

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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)