State Encoders in Reinforcement Learning for Recommendation-Reference-Cited by-同舟云学术

State Encoders in Reinforcement Learning for Recommendation

Published:2022-07-06 Issue: Volume: Page:
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Container-title:Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Huang Jin¹,Oosterhuis Harrie²,Cetinkaya Bunyamin¹,Rood Thijs¹,de Rijke Maarten¹

Affiliation:

1. University of Amsterdam, Amsterdam, Netherlands

2. Radboud University, Nijmegen, Netherlands

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3477495.3531716

Reference68 articles.

1. Panagiotis Adamopoulos and Alexander Tuzhilin. 2014. On Over-Specialization and Concentration Bias of Recommendations: Probabilistic Neighborhood Selection in Collaborative Filtering Systems. In RecSys . ACM 153--160. Panagiotis Adamopoulos and Alexander Tuzhilin. 2014. On Over-Specialization and Concentration Bias of Recommendations: Probabilistic Neighborhood Selection in Collaborative Filtering Systems. In RecSys . ACM 153--160.

2. M. Mehdi Afsar , Trafford Crump , and Behrouz Far . 2021. Reinforcement Learning based Recommender Systems: A Survey. arXiv preprint arXiv:2101.06286 ( 2021 ). M. Mehdi Afsar, Trafford Crump, and Behrouz Far. 2021. Reinforcement Learning based Recommender Systems: A Survey. arXiv preprint arXiv:2101.06286 (2021).

3. Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In ICLR . Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In ICLR .

4. Learning long-term dependencies with gradient descent is difficult

5. Lucas Bernardi , Sakshi Batra , and Cintia Alicia Bruscantini . 2021. Simulations in Recommender Systems: An Industry Perspective. arXiv preprint arXiv:2109.06723 ( 2021 ). Lucas Bernardi, Sakshi Batra, and Cintia Alicia Bruscantini. 2021. Simulations in Recommender Systems: An Industry Perspective. arXiv preprint arXiv:2109.06723 (2021).

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