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Fifty Shades of Ratings : How to Benefit from a Negative..:
, In:
Proceedings of the 10th ACM Conference on Recommender Systems
,
Frolov, Evgeny
;
Oseledets, Ivan
- p. 91-98 , 2016
Link:
https://dl.acm.org/doi/10.1145/2959100.2959170
RT T1 <cTitle>Proceedings of the 10th ACM Conference on Recommender Systems</cTitle>: T1 <Title>Fifty Shades of Ratings : How to Benefit from a Negative Feedback in Top-N Recommendations Tasks</Title> UL https://suche.suub.uni-bremen.de/peid=acm-2959170&Exemplar=1&LAN=DE A1 Frolov, Evgeny A1 Oseledets, Ivan PB ACM YR 2016 K1 cold-start K1 collaborative filtering K1 explicit feedback K1 recommender systems K1 tensor factorization K1 Human-centered computing K1 Collaborative and social computing K1 Collaborative and social computing theory, concepts and paradigms K1 Collaborative filtering K1 Information systems K1 Information retrieval K1 Retrieval models and ranking K1 Learning to rank K1 Novelty in information retrieval SP 91 OP 98 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/2959100.2959170 DO https://dl.acm.org/doi/10.1145/2959100.2959170 SF ELIB - SuUB Bremen
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