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The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores.
Modern information retrieval
R. Baeza-Yates and B. Ribeiro-Neto · 1999
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Statistical Analysis with Missing Data
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Accurately interpreting clickthrough data as implicit feedback
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Learning to rank: From pairwise approach to listwise approach
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Beyond position bias: Examining result attractiveness as a source of presentation bias in clickthrough data
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Deep reinforcement learning with attention for slate markov decision processes with high-dimensional states and actions
Peter Sunehag, Richard Evans, Gabriel Dulac-Arnold, Yori Zwols, Daniel Visentin, and Ben Coppin · 2015
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Collaborative denoising auto-encoders for top-n recommender systems
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Beyond ranking: Optimizing whole-page presentation
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Off-policy evaluation for slate recommendation
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Collaborative variational autoencoder for recommender systems
Xiaopeng Li and James She · 2017
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Augmented variational autoencoders for collaborative filtering with auxiliary information
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Hao Wang, Naiyan Wang, and Dit-Yan Yeung · 2015
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Recsys challenge 2015 and the yoochoose dataset, 2015
David Ben-Shimon, Michael Friedman, Alexander Tsikinovsky, and Johannes Hörle · 2015
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