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Most practical recommender systems focus on estimating immediate user engagement without considering the long-term effects of recommendations on user behavior.
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Mukund Deshpande and George Karypis · 2004
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Usage-based web recommendations: A reinforcement learning approach
Nima Taghipour, Ahmad Kardan, and Saeed Shiry Ghidary · 2007
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An experimental comparison of click position-bias models
Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey · 2008
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Latent Dirichlet allocation for tag recommendation
Ralf Krestel, Peter Fankhauser, and Wolfgang Nejdl · 2009
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Learning to rank for information retrieval
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A theoretical and empirical analysis of expected SARSA
Harm Van Seijen, Hado Van Hasselt, Shimon Whiteson, and Marco Wiering · 2009
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Deep neural networks for YouTube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
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The Netflix recommender system: Algorithms, business value, and innovation
Carlos A. Gomez-Uribe and Neil Hunt · 2016
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Fusing similarity models with Markov chains for sparse sequential recommendation
Ruining He and Julian McAuley · 2016
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Music personalization at Spotify
Kurt Jacobson, Vidhya Murali, Edward Newett, Brian Whitman, and Romain Yon · 2016
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Improved recurrent neural networks for session-based recommendations
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Competing for shelf space
Victor Martínez-de Albéniz and Guillaume Roels · 2010
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Factorizing personalized Markov chains for next-basket recommendation
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme · 2010
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On the optimal product line selection problem with price discrimination
Cornelia Schön · 2010
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Optimal Bayesian recommendation sets and myopically optimal choice query sets
Paolo Viappiani and Craig Boutilier · 2010
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Handling data sparsity in collaborative filtering using emotion and semantic based features
Yashar Moshfeghi, Benjamin Piwowarski, and Joemon M. Jose · 2011
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Optimal algorithms for assortment selection under ranking-based consumer choice models
Dorothee Honhon, Sreelata Jonnalagedda, and Xiajun Amy Pan · 2012
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Yong Kiam Tan, Xinxing Xu, and Yong Liu · 2016
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Automatic representation for lifetime value recommender systems
Assaf Hallak, Yishay Mansour, and Elad Yom-Tov · 2017
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Indexable Bayesian personalized ranking for efficient top-k recommendation
Dung D. Le and Hady W. Lauw · 2017
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Discrete sequential prediction of continuous actions for deep RL
Luke Metz, Julian Ibarz, Navdeep Jaitly, and James Davidson · 2017
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Off-policy evaluation for slate recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, and Imed Zitouni · 2017
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Recurrent recommender networks
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing · 2017
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
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Learning a deep listwise context model for ranking refinement
Qingyao Ai, Keping Bi, Jiafeng Guo, and W. Bruce Croft · 2018
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Seq2slate: Re-ranking and slate optimization with rnns
Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Chi, Elad Eban, Xiyang Luo, Alan Mackey, and Ofer Meshi · 2018
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Planning and learning with stochastic action sets
Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, and Dale Schuurmans · 2018
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Dopamine: A research framework for deep reinforcement learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G. Bellemare · 2018
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Top-k off-policy correction for a REINFORCE recommender system
Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed Chi · 2018
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Reinforcement learning-based recommender system using biclustering technique
Sungwoon Choi, Heonseok Ha, Uiwon Hwang, Chanju Kim, Jung-Woo Ha, and Sungroh Yoon · 2018
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Horizon: Facebook’s open source applied reinforcement learning platform
Jason Gauci, Edoardo Conti, Yitao Liang, Kittipat Virochsiri, Yuchen He, Zachary Kaden, Vivek Narayanan, and Xiaohui Ye · 2018
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Practical diversified recommendations on YouTube with determinantal point processes
Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H. Chi, and Jennifer Gillenwater · 2018
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Deep reinforcement learning for page-wise recommendations
Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang · 2018
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SlateQ: A tractable decomposition for reinforcement learning with recommendation sets
Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Tushar Chandra, and Craig Boutilier · 2019
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Beyond greedy ranking: Slate optimization via List-CVAE
Ray Jiang, Sven Gowal, Timothy A. Mann, and Danilo J. Rezende · 2019
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Jointly leveraging intent and interaction signals to predict user satisfaction with slate recommendations
Rishabh Mehrotra, Mounia Lalmas, Doug Kenney, Thomas Lim-Meng, and Golli Hashemian · 2019
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