Learning Attribute-to-Feature Mappings for Cold-Start Recommendations


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GANTNER, Zeno, Lucas DRUMOND, Christoph FREUDENTHALER, Steffen RENDLE, Lars SCHMIDT-THIEME, 2010. Learning Attribute-to-Feature Mappings for Cold-Start Recommendations. 2010 IEEE 10th International Conference on Data Mining (ICDM). Sydney, Australia, 13. Dez 2010 - 17. Dez 2010. In: 2010 IEEE International Conference on Data Mining. 2010 IEEE 10th International Conference on Data Mining (ICDM). Sydney, Australia, 13. Dez 2010 - 17. Dez 2010. IEEE, pp. 176-185. ISBN 978-1-4244-9131-5

@inproceedings{Gantner2010-12Learn-12687, title={Learning Attribute-to-Feature Mappings for Cold-Start Recommendations}, year={2010}, doi={10.1109/ICDM.2010.129}, isbn={978-1-4244-9131-5}, publisher={IEEE}, booktitle={2010 IEEE International Conference on Data Mining}, pages={176--185}, author={Gantner, Zeno and Drumond, Lucas and Freudenthaler, Christoph and Rendle, Steffen and Schmidt-Thieme, Lars} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/12687"> <dc:contributor>Rendle, Steffen</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-09-08T06:24:31Z</dcterms:available> <dc:contributor>Gantner, Zeno</dc:contributor> <dc:creator>Rendle, Steffen</dc:creator> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dc:creator>Schmidt-Thieme, Lars</dc:creator> <dc:creator>Gantner, Zeno</dc:creator> <dc:rights>deposit-license</dc:rights> <dcterms:bibliographicCitation>First publ. in: 2010 IEEE 10th International Conference on Data Mining (ICDM 2010) : Sydney, Australia, 13 - 17 December 2010 ; [proceedings] / [IEEE Computer Society]. Ed.: Geoffrey I. Webb ... . Piscataway, NJ : IEEE, 2010, pp. 176-185</dcterms:bibliographicCitation> <dcterms:issued>2010-12</dcterms:issued> <dc:creator>Freudenthaler, Christoph</dc:creator> <dcterms:abstract xml:lang="eng">Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and the new-item problem, while retaining its advantages, in particular speed and predictive accuracy. We use the mapping concept to construct an attribute-aware matrix factorization model for item recommendation from implicit, positive-only feedback. Experiments on the new-item problem show that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor.</dcterms:abstract> <dcterms:title>Learning Attribute-to-Feature Mappings for Cold-Start Recommendations</dcterms:title> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/12687"/> <dc:contributor>Schmidt-Thieme, Lars</dc:contributor> <dc:creator>Drumond, Lucas</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-09-08T06:24:31Z</dc:date> <dc:contributor>Drumond, Lucas</dc:contributor> <dc:contributor>Freudenthaler, Christoph</dc:contributor> <dc:language>eng</dc:language> </rdf:Description> </rdf:RDF>

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