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An Empirical Study on Recommendation with Multiple Types of..:

, In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,

Tang, Liang ; Long, Bo ; Chen, Bee-Chung. - p. 283-292 , 2016

Link: https://dl.acm.org/doi/10.1145/2939672.2939690

RT 
T1 <cTitle>Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</cTitle>: T1 <Title>An Empirical Study on Recommendation with Multiple Types of Feedback</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-2939690&Exemplar=1&LAN=DE
				
A1 Tang, Liang
A1 Long, Bo
A1 Chen, Bee-Chung
A1 Agarwal, Deepak
PB ACM
YR 2016
K1 multi-objective optimization
K1 personalized recommendation
K1 recommender system
K1 Information systems
K1 World Wide Web
K1 Web searching and information discovery
K1 Personalization
K1 Information retrieval
K1 Retrieval tasks and goals
K1 Recommender systems
K1 Human-centered computing
K1 Collaborative and social computing
K1 Collaborative and social computing theory, concepts and paradigms
K1 Social recommendation
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Multi-task learning
SP 283
OP 292
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/2939672.2939690
DO https://dl.acm.org/doi/10.1145/2939672.2939690
SF ELIB - SuUB Bremen

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