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1 Ergebnisse
1
Toward Pareto Efficient Fairness-Utility Trade-off in Recom..:
, In:
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
,
Ge, Yingqiang
;
Zhao, Xiaoting
;
Yu, Lucia
... - p. 316-324 , 2022
Link:
https://dl.acm.org/doi/10.1145/3488560.3498487
RT T1 <cTitle>Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining</cTitle>: T1 <Title>Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3498487&Exemplar=1&LAN=DE A1 Ge, Yingqiang A1 Zhao, Xiaoting A1 Yu, Lucia A1 Paul, Saurabh A1 Hu, Diane A1 Hsieh, Chu-Cheng A1 Zhang, Yongfeng PB ACM YR 2022 K1 multi-objective reinforcement learning K1 pareto efficient fairness K1 recommender system K1 unbiased recommendation K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Reinforcement learning K1 Sequential decision making K1 Information systems K1 Information retrieval K1 Retrieval tasks and goals K1 Recommender systems SP 316 OP 324 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3488560.3498487 DO https://dl.acm.org/doi/10.1145/3488560.3498487 SF ELIB - SuUB Bremen
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