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.
Description
Matrix Factorization Techniques for Recommender Systems
%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 final thema:playlistpredictionviametricembedding
%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 = {2013-11-08T23:17:43.000+0100},
address = {Los Alamitos, CA, USA},
author = {Koren, Yehuda and Bell, Robert and Volinsky, Chris},
biburl = {https://www.bibsonomy.org/bibtex/26375c7802c40dd797c7b043f873b8a8f/florian.pf},
description = {Matrix Factorization Techniques for Recommender Systems},
doi = {10.1109/MC.2009.263},
interhash = {cface72aeba6ee8c561ccd15035d0ead},
intrahash = {6375c7802c40dd797c7b043f873b8a8f},
issn = {0018-9162},
issue_date = {August 2009},
journal = {Computer},
keywords = {final thema:playlistpredictionviametricembedding},
month = aug,
number = 8,
numpages = {8},
pages = {30--37},
publisher = {IEEE Computer Society Press},
timestamp = {2013-12-10T12:29:40.000+0100},
title = {Matrix Factorization Techniques for Recommender Systems},
url = {http://dx.doi.org/10.1109/MC.2009.263},
volume = 42,
year = 2009
}