Fetching the paper…
Reading the bibliography…
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications.
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. 2019b · 1909
Earlier work this paper cites.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1930–1939
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018b · 1939
Earlier work this paper cites.
Learning to collaborate: Multi-scenario ranking via multi-agent reinforcement learning. In Proceedings of the 2018 World Wide Web Conference . 1939–1948
Jun Feng, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, and Xiaoyan Zhu. 2018 · 1948
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
Earlier work this paper cites.
Webwatcher: A tour guide for the world wide web. In IJCAI (1) . Citeseer, 770–777
Thorsten Joachims, Dayne Freitag, Tom Mitchell, et al · 1997
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour. 1999 · 1999
Earlier work this paper cites.
Content-based book recommending using learning for text categorization. In Proceedings of the fifth ACM conference on Digital libraries . 195–204
Raymond J Mooney and Loriene Roy. 2000 · 2000
Earlier work this paper cites.
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Wen-Ji Zhou, Qing Da, Anxiang Zeng, and Yang Yu. 2020 · 2003
Earlier work this paper cites.
An MDP-based recommender system
Guy Shani, David Heckerman, Ronen I Brafman, and Craig Boutilier. 2005 · 2005
Earlier work this paper cites.
Incremental natural actor-critic algorithms
Shalabh Bhatnagar, Mohammad Ghavamzadeh, Mark Lee, and Richard S Sutton. 2007 · 2007
Earlier work this paper cites.
Learning and adaptivity in interactive recommender systems. In Proceedings of the ninth international conference on Electronic commerce . 75–84
Tariq Mahmood and Francesco Ricci. 2007 · 2007
Earlier work this paper cites.
Usage-based web recommendations: a reinforcement learning approach. In Proceedings of the 2007 ACM conference on Recommender systems . 113–120
Nima Taghipour, Ahmad Kardan, and Saeed Shiry Ghidary. 2007 · 2007
Earlier work this paper cites.
Natural actor-critic
Jan Peters and Stefan Schaal. 2008 · 2008
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Learning to rank for information retrieval
Tie-Yan Liu et al · 2009
Earlier work this paper cites.
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
Yufan Zhao, Donglin Zeng, Mark A Socinski, and Michael R Kosorok. 2011 · 2011
Earlier work this paper cites.
Model-free reinforcement learning with continuous action in practice. In 2012 American Control Conference (ACC) . IEEE, 2177–2182
Thomas Degris, Patrick M Pilarski, and Richard S Sutton. 2012 · 2012
Earlier work this paper cites.
Optimal radio channel recommendations with explicit and implicit feedback. In Proceedings of the sixth ACM conference on Recommender systems . 75–82
Omar Moling, Linas Baltrunas, and Francesco Ricci. 2012 · 2012
Earlier work this paper cites.
Deterministic policy gradient algorithms. In International conference on machine learning . Pmlr, 387–395
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller. 2014 · 2014
Earlier work this paper cites.
Deep reinforcement learning in large discrete action spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, and Ben Coppin. 2015 · 2015
Cited alongside, same era.
Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 2: short papers) . 845–850
Long Duong, Trevor Cohn, Steven Bird, and Paul Cook. 2015 · 2015
Cited alongside, same era.
Multi-task sequence to sequence learning
Minh-Thang Luong, Quoc V Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2015 · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015 · 2015
Cited alongside, same era.
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. 2019a · 2019
Later among the works it cites.
A Mandarin Prosodic Boundary Prediction Model Based on Multi-Task Learning.. In Interspeech . 4485–4488
Huashan Pan, Xiulin Li, and Zhiqiang Huang. 2019 · 2019
Later among the works it cites.
Value-aware recommendation based on reinforcement profit maximization. In The World Wide Web Conference . 3123–3129
Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, and Yongfeng Zhang. 2019 · 2019
Later among the works it cites.
Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019 · 2019
Later among the works it cites.
" Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Cited alongside, same era.
Real-time video recommendation exploration. In Proceedings of the 2016 International Conference on Management of Data . 35–46
Yanxiang Huang, Bin Cui, Jie Jiang, Kunqian Hong, Wenyu Zhang, and Yiran Xie. 2016 · 2016
Cited alongside, same era.
Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition . 3994–4003
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016 · 2016
Cited alongside, same era.
Deep multi-task representation learning: A tensor factorisation approach
Yongxin Yang and Timothy Hospedales. 2016 · 2016
Cited alongside, same era.
Neural network methods for natural language processing
Yoav Goldberg. 2017 · 2017
Cited alongside, same era.
DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Deep Reinforcement Learning for List-wise Recommendations
Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Dawei Yin, Yihong Zhao, and Jiliang Tang. 2017 · 2017
Cited alongside, same era.
Rank and rate: multi-task learning for recommender systems. In Proceedings of the 12th ACM Conference on Recommender Systems . 451–454
Guy Hadash, Oren Sar Shalom, and Rita Osadchy. 2018 · 2018
Cited alongside, same era.
Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin. 2019 · 2019
Later among the works it cites.
Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, and Dawei Yin. 2019 · 2019
Later among the works it cites.
Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0
Giuseppe Aceto, Valerio Persico, and Antonio Pescapé. 2020 · 2020
Later among the works it cites.
State representation modeling for deep reinforcement learning based recommendation
Feng Liu, Ruiming Tang, Xutao Li, Weinan Zhang, Yunming Ye, Haokun Chen, Huifeng Guo, Yuzhou Zhang, and Xiuqiang He. 2020b · 2020
Later among the works it cites.
Kalman filtering attention for user behavior modeling in ctr prediction
Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, et al · 2020
Later among the works it cites.
Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Fourteenth ACM Conference on Recommender Systems . 269–278
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020 · 2020
Later among the works it cites.
A survey of multi-task deep reinforcement learning
Nelson Vithayathil Varghese and Qusay H Mahmoud. 2020 · 2020
Later among the works it cites.
Reinforcement learning based recommender systems: A survey
M Mehdi Afsar, Trafford Crump, and Behrouz Far. 2021 · 2021
Later among the works it cites.
User response models to improve a reinforce recommender system. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 121–129
Minmin Chen, Bo Chang, Can Xu, and Ed H Chi. 2021 · 2021
Later among the works it cites.
A survey on multi-task learning
Yu Zhang and Qiang Yang. 2021 · 2021
Later among the works it cites.
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 750–758
Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Hui Liu, and Jiliang Tang. 2021 · 2021
Later among the works it cites.
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (Atlanta, GA, USA) (CIKM ’22) . 5 pages
Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Wenqiang Lei, Peng Jiang, and Xiangnan He. 2022 · 2022
Later among the works it cites.
Surrogate for Long-Term User Experience in Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4100–4109
Yuyan Wang, Mohit Sharma, Can Xu, Sriraj Badam, Qian Sun, Lee Richardson, Lisa Chung, Ed H Chi, and Minmin Chen. 2022 · 2022
Later among the works it cites.
Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4510–4520
Qihua Zhang, Junning Liu, Yuzhuo Dai, Yiyan Qi, Yifan Yuan, Kunlun Zheng, Fan Huang, and Xianfeng Tan. 2022 · 2022
Later among the works it cites.