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The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems.
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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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Yehuda Koren, Robert Bell and Chris Volinsky · 2009
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Tie-Yan Liu · 2009
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“The probabilistic relevance framework: BM25 and beyond”
Stephen Robertson and Hugo Zaragoza · 2009
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“From ranknet to lambdarank to lambdamart: An overview”
Christopher Burges · 2010
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“BPR: Bayesian personalized ranking from implicit feedback”
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner and Lars Schmidt-Thieme · 2012
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“On using very large target vocabulary for neural machine translation”
Sébastien Jean, Kyunghyun Cho, Roland Memisevic and Yoshua Bengio · 2014
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“Deep Neural Networks for YouTube Recommendations”
Paul Covington, Jay Adams and Emre Sargin · 2016
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“Deep neural networks for youtube recommendations”
Paul Covington, Jay Adams and Emre Sargin · 2016
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“Ranking relevance in yahoo search”
Dawei Yin et al · 2016
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“Unbiased learning-to-rank with biased feedback”
Thorsten Joachims, Adith Swaminathan and Tobias Schnabel · 2017
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“Accurately interpreting clickthrough data as implicit feedback”
Thorsten Joachims et al · 2017
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“Cascade ranking for operational e-commerce search”
Shichen Liu, Fei Xiao, Wenwu Ou and Luo Si · 2017
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“Self-attentive sequential recommendation”
Wang-Cheng Kang and Julian McAuley · 2018
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“Deep interest network for click-through rate prediction”
Guorui Zhou et al · 2018
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“Policy-aware unbiased learning to rank for top-k rankings”
Harrie Oosterhuis and Maarten de Rijke · 2020
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“Correcting for selection bias in learning-to-rank systems”
Zohreh Ovaisi et al · 2020
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“Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction”
Qi Pi et al · 2020
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“Unbiased pairwise learning from biased implicit feedback”
Yuta Saito · 2020
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“Cold: Towards the next generation of pre-ranking system”
Zhe Wang et al · 2020
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“GRN: Generative Rerank Network for Context-wise Recommendation”
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Han Zhu et al · 2018
Cited alongside, same era.
“Top-k off-policy correction for a REINFORCE recommender system”
Minmin Chen et al · 2019
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“Joint optimization of cascade ranking models”
Luke Gallagher, Ruey-Cheng Chen, Roi Blanco and J Culpepper · 2019
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“Unbiased lambdamart: an unbiased pairwise learning-to-rank algorithm”
Ziniu Hu, Yang Wang, Qu Peng and Hang Li · 2019
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“SLATEQ: a tractable decomposition for reinforcement learning with recommendation sets”
Eugene Ie et al · 2019
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“Multi-Interest Network with Dynamic Routing for Recommendation at Tmall”
Chao Li et al · 2019
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Yufei Feng et al · 2021
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“Pairrank: Online pairwise learning to rank by divide-and-conquer”
Yiling Jia, Huazheng Wang, Stephen Guo and Hongning Wang · 2021
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“Propensity-independent bias recovery in offline learning-to-rank systems”
Zohreh Ovaisi, Kathryn Vasilaky and Elena Zheleva · 2021
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“Contrastive learning for debiased candidate generation in large-scale recommender systems”
Chang Zhou et al · 2021
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“KuaiRec: A Fully-Observed Dataset and Insights for Evaluating Recommender Systems”
Chongming Gao et al · 2022
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“RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows”
Jiarui Qin et al · 2022
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“Pepnet: Parameter and embedding personalized network for infusing with personalized prior information”
Jianxin Chang et al · 2023
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“TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou”
Jianxin Chang et al · 2023
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Yuan Zhang et al · 2023
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