MGPolicy
Author:
Affiliation:
1. University of Technology Sydney, Sydney, NSW, Australia
2. Curtin University, Perth, WA, Australia
3. University of New South Wales, Sydney, NSW, Australia
Funder
Australian Research Council
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3477495.3532021
Reference43 articles.
1. Top-K Off-Policy Correction for a REINFORCE Recommender System
2. Xiaocong Chen , Lina Yao , Julian McAuley , Guangling Zhou , and Xianzhi Wang . 2021. A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions. arXiv preprint arXiv:2109.03540 ( 2021 ). Xiaocong Chen, Lina Yao, Julian McAuley, Guangling Zhou, and Xianzhi Wang. 2021. A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions. arXiv preprint arXiv:2109.03540 (2021).
3. Block- Aware Item Similarity Models for Top-N Recommendation;Chen Yifan;ACM Transactions on Information Systems (TOIS),2020
4. Recommendation system based on deep learning methods: a systematic review and new directions
5. Adaptive subgradient methods for online learning and stochastic optimization;Duchi John;Journal of machine learning research,2011
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