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A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems.

, , and . UMAP, page 280-284. ACM, (2017)

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Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation, and . Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, page 71-79. ACM, (July 2020)Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks., and . CoRR, (2018)Änd the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation., , , , and . CoRR, (2020)Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems., , and . UMAP, page 225-233. ACM, (2019)Collaborative Recommendation of Informal Learning Experiences., and . WI Workshops, page 66-67. IEEE Computer Society, (2016)Opportunistic Multi-aspect Fairness through Personalized Re-ranking., , , , and . UMAP, page 239-247. ACM, (2020)A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems., , and . UMAP, page 280-284. ACM, (2017)User Segmentation for Controlling Recommendation Diversity., , and . RecSys Posters, volume 1688 of CEUR Workshop Proceedings, CEUR-WS.org, (2016)Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models., and . FLAIRS, page 425-430. AAAI Press, (2019)