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Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems.
Parametric bandits: The generalized linear case. In NIPS . 586–594
Sarah Filippi, Olivier Cappe, Aurélien Garivier, and Csaba Szepesvári. 2010 · 2010
Earlier work this paper cites.
A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web . ACM, 661–670
Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010 · 2010
Earlier work this paper cites.
Improved algorithms for linear stochastic bandits. In Advances in Neural Information Processing Systems . 2312–2320
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári. 2011 · 2011
Earlier work this paper cites.
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. In Proceedings of the fourth ACM international conference on Web search and data mining . ACM, 297–306
Lihong Li, Wei Chu, John Langford, and Xuanhui Wang. 2011 · 2011
Earlier work this paper cites.
Hierarchical exploration for accelerating contextual bandits
Yisong Yue, Sue Ann Hong, and Carlos Guestrin. 2012 · 2012
Earlier work this paper cites.
Thompson sampling for contextual bandits with linear payoffs. In International Conference on Machine Learning . 127–135
Shipra Agrawal and Navin Goyal. 2013 · 2013
Earlier work this paper cites.
A gang of bandits. In Advances in Neural Information Processing Systems . 737–745
Nicolo Cesa-Bianchi, Claudio Gentile, and Giovanni Zappella. 2013 · 2013
Cited alongside, same era.
Towards conversational recommender systems. In KDD . ACM, 815–824
Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann. 2016 · 2016
Cited alongside, same era.
Collaborative filtering bandits. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . ACM, 539–548
Shuai Li, Alexandros Karatzoglou, and Claudio Gentile. 2016 · 2016
Cited alongside, same era.
Learning hidden features for contextual bandits. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management . ACM, 1633–1642
Huazheng Wang, Qingyun Wu, and Hongning Wang. 2016 · 2016
Cited alongside, same era.
Contextual bandits in a collaborative environment. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . ACM, 529–538
Q&R: A two-stage approach toward interactive recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 139–148
Konstantina Christakopoulou, Alex Beutel, Rui Li, Sagar Jain, and Ed H Chi. 2018 · 2018
Later among the works it cites.
Conversational Recommender System
Yueming Sun and Yi Zhang. 2018 · 2018
Later among the works it cites.
Towards conversational search and recommendation: System ask, user respond. In Proceedings of CIKM . ACM, 177–186
Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W Bruce Croft. 2018 · 2018
Later among the works it cites.
An Visual Dialog Augmented Interactive Recommender System. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 157–165
Tong Yu, Yilin Shen, and Hongxia Jin. 2019 · 2019
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Qingyun Wu, Huazheng Wang, Quanquan Gu, and Hongning Wang. 2016 · 2016
Cited alongside, same era.
Active Learning in Recommendation Systems with Multi-level User Preferences
Yuheng Bu and Kevin Small. 2018 · 2018
Cited alongside, same era.
Bandit algorithms
Tor Lattimore and Csaba Szepesvári. [n.d.]
Cited in the paper.
Online context-aware recommendation with time varying multi-armed bandit. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2025–2034
Chunqiu Zeng, Qing Wang, Shekoofeh Mokhtari, and Tao Li. 2016 · 2034
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