%0 Journal Article %1 Koren:2009:MFT:1608565.1608614 %A Koren, Yehuda %A Bell, Robert %A Volinsky, Chris %C Los Alamitos, CA, USA %D 2009 %I IEEE Computer Society Press %J Computer %K %N 8 %P 30--37 %R 10.1109/MC.2009.263 %T Matrix Factorization Techniques for Recommender Systems %U http://dx.doi.org/10.1109/MC.2009.263 %V 42 %X As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.

@article{Koren:2009:MFT:1608565.1608614, abstract = {As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.}, acmid = {1608614}, added-at = {2013-11-08T23:19:52.000+0100}, address = {Los Alamitos, CA, USA}, author = {Koren, Yehuda and Bell, Robert and Volinsky, Chris}, biburl = {https://www.bibsonomy.org/bibtex/26375c7802c40dd797c7b043f873b8a8f/florian.pf}, doi = {10.1109/MC.2009.263}, interhash = {cface72aeba6ee8c561ccd15035d0ead}, intrahash = {6375c7802c40dd797c7b043f873b8a8f}, issn = {0018-9162}, issue_date = {August 2009}, journal = {Computer}, keywords = {}, month = aug, number = 8, numpages = {8}, pages = {30--37}, publisher = {IEEE Computer Society Press}, timestamp = {2013-11-08T23:19:52.000+0100}, title = {Matrix Factorization Techniques for Recommender Systems}, url = {http://dx.doi.org/10.1109/MC.2009.263}, volume = 42, year = 2009 }