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As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS.
The analysis of permutations
Robin L Plackett · 1975
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
The use of mmr, diversity-based reranking for reordering documents and producing summaries
Jaime Carbonell and Jade Goldstein · 1998
Earlier work this paper cites.
Classification with a reject option using a hinge loss
Peter L Bartlett and Marten H Wegkamp · 2008
Earlier work this paper cites.
Listwise approach to learning to rank: theory and algorithm
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li · 2008
Earlier work this paper cites.
From ranknet to lambdarank to lambdamart: An overview
Christopher JC Burges · 2010
Earlier work this paper cites.
Bpr: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2012
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Earlier work this paper cites.
A neural click model for web search
Alexey Borisov, Ilya Markov, Maarten De Rijke, and Pavel Serdyukov · 2016
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Modeling document novelty with neural tensor network for search result diversification
Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng · 2016
Earlier work this paper cites.
Neural network methods for natural language processing
Yoav Goldberg · 2017
Earlier work this paper cites.
Learning to diversify search results via subtopic attention
Zhengbao Jiang, Ji-Rong Wen, Zhicheng Dou, Wayne Xin Zhao, Jian-Yun Nie, and Ming Yue · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, et al · 2017
Earlier work this paper cites.
Adapting markov decision process for search result diversification
Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng · 2017
Earlier work this paper cites.
Learning a deep listwise context model for ranking refinement
Qingyao Ai, Keping Bi, Jiafeng Guo, and W. Croft · 2018
Earlier work this paper cites.
Seq2slate: Re-ranking and slate optimization with rnns, 2018
Irwan Bello, Sayali Kulkarni, Sagar Jain, et al · 2018
Earlier work this paper cites.
From greedy selection to exploratory decision-making: Diverse ranking with policy-value networks
Yue Feng, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng · 2018
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, et al · 2018
Earlier work this paper cites.
Beyond greedy ranking: Slate optimization via list-cvae
Ray Jiang, Sven Gowal, Yuqiu Qian, Timothy Mann, and Danilo J Rezende · 2018
Earlier work this paper cites.
Using image fairness representations in diversity-based re-ranking for recommendations
Chen Karako and Putra Manggala · 2018
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The lambdaloss framework for ranking metric optimization
Xuanhui Wang, Cheng Li, Nadav Golbandi, Michael Bendersky, and Marc Najork · 2018
Cited alongside, same era.
Practical diversified recommendations on youtube with determinantal point processes
Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, et al · 2018
Cited alongside, same era.
Globally optimized mutual influence aware ranking in e-commerce search
Tao Zhuang, Wenwu Ou, and Zhirong Wang · 2018
Cited alongside, same era.
Learning groupwise multivariate scoring functions using deep neural networks
Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, and Marc Najork · 2019
Cited alongside, same era.
Co-displayed items aware list recommendation
Junshuai Song, Zhao Li, Chang Zhou, et al · 2020
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Generator and critic: A deep reinforcement learning approach for slate re-ranking in e-commerce
Jianxiong Wei, Anxiang Zeng, Yueqiu Wu, Peng Guo, Qingsong Hua, and Qingpeng Cai · 2020
Later among the works it cites.
Jointly learning to recommend and advertise
Xiangyu Zhao, Xudong Zheng, Xiwang Yang, Xiaobing Liu, and Jiliang Tang · 2020
Later among the works it cites.
Revisit recommender system in the permutation prospective
Yufei Feng, Yu Gong, Fei Sun, Junfeng Ge, and Wenwu Ou · 2021
Later among the works it cites.
Revisit recommender system in the permutation prospective, 2021
Yufei Feng, Yu Gong, Fei Sun, Qingwen Liu, and Wenwu Ou · 2021
Later among the works it cites.
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On component interactions in two-stage recommender systems
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Variation control and evaluation for generative slate recommendations
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Computationally efficient optimization of plackett-luce ranking models for relevance and fairness
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Context-aware reranking with utility maximization for recommendation
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Hierarchical reinforcement learning for integrated recommendation
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Policy-gradient training of fair and unbiased ranking functions
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Diversification-aware learning to rank using distributed representation
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Constructing a comparison-based click model for web search
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Dear: Deep reinforcement learning for online advertising impression in recommender systems
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Fairness among new items in cold start recommender systems
Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, and James Caverlee · 2021
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Zhuoyi Lin, Sheng Zang, Rundong Wang, et al · 2022
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