Fetching the paper…
Reading the bibliography…
Recent E-commerce applications benefit from the growth of deep learning techniques.
Optimizing search engines using clickthrough data. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada . 133–142
Thorsten Joachims. 2002 · 2002
Earlier work this paper cites.
Learning to rank using gradient descent. In Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005) . 89–96
Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Gregory N. Hullender. 2005 · 2005
Earlier work this paper cites.
Being accurate is not enough: how accuracy metrics have hurt recommender systems. In CHI’06 extended abstracts on Human factors in computing systems . 1097–1101
Sean M McNee, John Riedl, and Joseph A Konstan. 2006 · 2006
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach. In Machine Learning, Proceedings of the Twenty-Fourth International Conference (ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007 . 129–136
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007 · 2007
Earlier work this paper cites.
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting. In Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007 . 897–904
Ping Li, Christopher J. C. Burges, and Qiang Wu. 2007 · 2007
Earlier work this paper cites.
Statistical Analysis of Bayes Optimal Subset Ranking
David Cossock and Tong Zhang. 2008 · 2008
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining . Ieee, 263–272
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
Earlier work this paper cites.
Listwise approach to learning to rank: theory and algorithm. In Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008 . 1192–1199
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008 · 2008
Earlier work this paper cites.
From ranknet to lambdarank to lambdamart: An overview
Christopher JC Burges. 2010 · 2010
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
Cited alongside, same era.
A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. In Proceedings of the international workshop on reproducibility and replication in recommender systems evaluation . 7–14
Joeran Beel, Marcel Genzmehr, Stefan Langer, Andreas Nürnberger, and Bela Gipp. 2013 · 2013
Cited alongside, same era.
Generative Adversarial Nets. In Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal . 2672–2680
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Guided cost learning: Deep inverse optimal control via policy optimization. In International Conference on Machine Learning . 49–58
Chelsea Finn, Sergey Levine, and Pieter Abbeel. 2016 · 2016
Cited alongside, same era.
David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou. 2018 · 2018
Later among the works it cites.
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks. In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval . ACM, 85–92
Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky, and Marc Najork. 2019 · 2019
Later among the works it cites.
Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems . 101–109
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019 · 2019
Later among the works it cites.
RecSim: A Configurable Simulation Platform for Recommender Systems
Eugene Ie, Chih-wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, and Craig Boutilier. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generative Adversarial Imitation Learning. In Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain . 4565–4573
Jonathan Ho and Stefano Ermon. 2016 · 2016
Cited alongside, same era.
Contrasting offline and online results when evaluating recommendation algorithms. In Proceedings of the 10th ACM conference on recommender systems . 31–34
Marco Rossetti, Fabio Stella, and Markus Zanker. 2016 · 2016
Cited alongside, same era.
Unbiased learning-to-rank with biased feedback. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining . 781–789
Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017 · 2017
Cited alongside, same era.
Learning a deep listwise context model for ranking refinement. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . ACM, 135–144
Qingyao Ai, Keping Bi, Jiafeng Guo, and W Bruce Croft. 2018 · 2018
Cited alongside, same era.
Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 4902–4909
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and An-Xiang Zeng. 2019 · 2019
Later among the works it cites.
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Yawen Liu, Weijie Shen, Wen-Ji Zhou, Qing Da, An-Xiang Zeng, Han Yu, Yang Yu, et al · 2020
Later among the works it cites.
PARS: Peers-Aware Recommender System. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20) . Association for Computing Machinery, New York, NY, USA, 2606–2612
Huiqiang Mao, Yanzhi Li, Chenliang Li, Di Chen, Xiaoqing Wang, and Yuming Deng. 2020 · 2020
Later among the works it cites.
Collaborative List-and-Pairwise Filtering from Implicit Feedback
Runlong Yu, Qi Liu, Yuyang Ye, Mingyue Cheng, Enhong Chen, and Jianhui Ma. 2020 · 2020
Later among the works it cites.