Popescul, A.; Ungar, L. H.; Lawrence, S. & Pennock, D. M.: Statistical Relational Learning for Document Mining.. ICDM. IEEE Computer Society, 2003, S. 275-282
[Volltext]
@inproceedings{conf/icdm/PopesculULP03,
author = {Popescul, Alexandrin and Ungar, Lyle H. and Lawrence, Steve and Pennock, David M.},
title = {Statistical Relational Learning for Document Mining.},
booktitle = {ICDM},
publisher = {IEEE Computer Society},
year = {2003},
pages = {275-282},
url = {http://www.cis.upenn.edu/~popescul/Publications/popescul03dm.pdf},
isbn = {0-7695-1978-4},
keywords = {document, 2003, classification, text, srl, tm, mining}
}
Schein, A. I.; Popescul, A.; Ungar, L. H. & Pennock, D. M.: Methods and Metrics for Cold-start Recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2002SIGIR '02 , S. 253-260
[Volltext]
We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.
@inproceedings{schein2002methods,
author = {Schein, Andrew I. and Popescul, Alexandrin and Ungar, Lyle H. and Pennock, David M.},
title = {Methods and Metrics for Cold-start Recommendations},
booktitle = {Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
series = {SIGIR '02},
publisher = {ACM},
address = {New York, NY, USA},
year = {2002},
pages = {253--260},
url = {http://doi.acm.org/10.1145/564376.564421},
doi = {10.1145/564376.564421},
isbn = {1-58113-561-0},
keywords = {recommender, metrics, start, groc, cold, croc},
abstract = {We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.}
}
Popescul, A.; Ungar, L.; Pennock, D. & Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. 17th Conference on Uncertainty in Artificial Intelligence. Seattle, Washington: 2001, S. 437-444
[Volltext]
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,
author = {Popescul, Alexandrin and Ungar, Lyle and Pennock, David and Lawrence, Steve},
title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments},
booktitle = {17th Conference on Uncertainty in Artificial Intelligence},
address = {Seattle, Washington},
year = {2001},
pages = {437--444},
url = {http://citeseer.ist.psu.edu/popescul01probabilistic.html},
keywords = {plsi, clustering, recommender, 3mode},
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...}
}
Popescul, A.; Ungar, L.; Pennock, D. & Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. 17th Conference on Uncertainty in Artificial Intelligence. Seattle, Washington: 2001, S. 437-444
[Volltext]
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,
author = {Popescul, Alexandrin and Ungar, Lyle and Pennock, David and Lawrence, Steve},
title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments},
booktitle = {17th Conference on Uncertainty in Artificial Intelligence},
address = {Seattle, Washington},
year = {2001},
pages = {437--444},
url = {http://citeseer.ist.psu.edu/popescul01probabilistic.html},
keywords = {clustering, todo, mode, three, network, 3mode},
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...}
}