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CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering.

, , , , , and . RecSys, page 139-146. ACM, (2012)

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Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering., , , and . RecSys, page 79-86. ACM, (2010)Collaborative temporal order modeling.. RecSys, page 313-316. ACM, (2011)Compact Embedding of Binary-coded Inputs and Outputs using Bloom Filters., and . ICLR (Workshop), OpenReview.net, (2017)One Person, One Model, One World: Learning Continual User Representation without Forgetting., , , , , , and . CoRR, (2020)Supervised Advantage Actor-Critic for Recommender Systems., , , and . CoRR, (2021)Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning., , , , and . CoRR, (2021)Latent User Intent Modeling for Sequential Recommenders., , , , , and . CoRR, (2022)On Interpretation and Measurement of Soft Attributes for Recommendation., , and . SIGIR, page 890-899. ACM, (2021)xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance., , , , and . RecSys, page 431-434. ACM, (2013)DLRS 2017: Second Workshop on Deep Learning for Recommender Systems., , , , , and . RecSys, page 370-371. ACM, (2017)