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Collaborative Filtering Bandits.

, , и . SIGIR, стр. 539-548. ACM, (2016)

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Konstantinos Karatzoglou

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Alexandros Daponte

 

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Recurrent Neural Networks with Top-k Gains for Session-based Recommendations, и . arXiv preprint arXiv:1706.03847, (2017)Multiple Outcome Supervised Latent Dirichlet Allocation for Expert Discovery in Online Forums., и . AAAI (Late-Breaking Developments), том WS-13-17 из AAAI Workshops, AAAI, (2013)Enhancing Top-N Item Recommendations by Peer Collaboration., , , , , и . SIGIR, стр. 1895-1900. ACM, (2022)Collaborative Filtering Method for Handling Diverse and Repetitive User-Item Interactions., , , и . HT, стр. 43-51. ACM, (2018)DLRS 2018: third workshop on deep learning for recommender systems., , , , , , и . RecSys, стр. 512-513. ACM, (2018)Deep Learning for Recommender Systems., и . RecSys, стр. 396-397. ACM, (2017)The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems., , , , , , и . RecSys, стр. 249-252. ACM, (2016)GAPfm: optimal top-n recommendations for graded relevance domains., , , , и . CIKM, стр. 2261-2266. ACM, (2013)A Genetic Algorithm to Discover Flexible Motifs with Support., , , и . ICDM Workshops, стр. 1153-1158. IEEE Computer Society, (2016)xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance., , , , и . RecSys, стр. 431-434. ACM, (2013)