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Association rules for recommendations with multiple items
Ghoshal, Abhijeet; Sarkar, Sumit - In: INFORMS journal on computing : JOC 26 (2014) 3, pp. 433-448
Persistent link: https://www.econbiz.de/10010399819
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Optimization-based approaches for maximizing aggregate recommendation diversity
Adomavicius, Gediminas; Kwon, YoungOk - In: INFORMS journal on computing : JOC 26 (2014) 2, pp. 351-369
Persistent link: https://www.econbiz.de/10010362437
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Shilling attack detection : a new approach for a trustworthy recommender system
Lee, Jong-seok; Zhu, Dan - In: INFORMS journal on computing : JOC 24 (2012) 1, pp. 117-131
Persistent link: https://www.econbiz.de/10009523286
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Classification, ranking, and top-K stability of recommendation algorithms
Adomavicius, Gediminas; Zhang, Jingjing - In: INFORMS journal on computing : JOC 28 (2016) 1, pp. 129-147
Persistent link: https://www.econbiz.de/10011453808
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Incorporating aggregate diversity in recommender systems using scalable optimization approaches
Muter, Ibrahim; Aytekin, Tevfik - In: INFORMS journal on computing : JOC 29 (2017) 3, pp. 405-421
Persistent link: https://www.econbiz.de/10011743358
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Understanding the impact of individual users' rating characteristics on the predictive accuracy of recommender systems
Cheng, Xiaoye; Zhang, Jingjing; Yan, Lu - In: INFORMS journal on computing : JOC 32 (2020) 2, pp. 303-320
Persistent link: https://www.econbiz.de/10012242760
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Integrating individual and aggregate diversity in top-n recommendation
Çanakoğlu, Ethem; Muter, İbrahim; Aytekin, Tevfik - In: INFORMS journal on computing : JOC 33 (2021) 1, pp. 300-318
Persistent link: https://www.econbiz.de/10012496403
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Location-aware real-time recommender systems for brick-and-mortar retailers
Zeng, Daniel; Liu, Yong; Yan, Ping; Yang, Yanwu - In: INFORMS journal on computing : JOC 33 (2021) 4, pp. 1608-1623
Persistent link: https://www.econbiz.de/10012796956