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Multi-objective recommender systems address the difficult task of recommending items that are relevant to multiple, possibly conflicting, criteria.
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Learning to rank with multiple objective functions. In Proceedings of the 20th international conference on World wide web
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Click shaping to optimize multiple objectives. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, and Xuanhui Wang. 2011 · 2011
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Christopher JC Burges, Krysta Marie Svore, Paul N Bennett, Andrzej Pastusiak, and Qiang Wu. 2011 · 2011
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Multi-objective optimization in learning to rank. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Na Dai, Milad Shokouhi, and Brian D Davison. 2011 · 2011
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Learning to re-rank: query-dependent image re-ranking using click data. In Proceedings of the 20th international conference on World wide web
Vidit Jain and Manik Varma. 2011 · 2011
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Learning to re-rank web search results with multiple pairwise features. In Proceedings of the fourth ACM international conference on Web search and data mining
Changsung Kang, Xuanhui Wang, Jiang Chen, Ciya Liao, Yi Chang, Belle Tseng, and Zhaohui Zheng. 2011 · 2011
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A game-theoretic machine learning approach for revenue maximization in sponsored search. In Twenty-Third International Joint Conference on Artificial Intelligence
Di He, Wei Chen, Liwei Wang, and Tie-Yan Liu. 2013 · 2013
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Multi-criteria recommender systems
Gediminas Adomavicius and YoungOk Kwon. 2015 · 2015
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Yunlong Jiao and Jean-Philippe Vert. 2016 · 2016
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Considering Supplier Relations and Monetization in Designing Recommendation Systems. In Proceedings of the 10th ACM Conference on Recommender Systems
Jan Krasnodebski and John Dines. 2016 · 2016
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