Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling

Author:

Wang Jie1ORCID,Karatzoglou Alexandros2ORCID,Arapakis Ioannis3ORCID,Jose Joemon M.1ORCID

Affiliation:

1. University of Glasgow, Glasgow, United Kingdom

2. Amazon, Barcelona, Spain

3. Telefonica Research, Barcelona, Spain

Publisher

ACM

Reference49 articles.

1. Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447 (2023).

2. Jin Chen Zheng Liu Xu Huang Chenwang Wu Qi Liu Gangwei Jiang Yuanhao Pu Yuxuan Lei Xiaolong Chen Xingmei Wang et al. 2023. When large language models meet personalization: Perspectives of challenges and opportunities. arXiv preprint arXiv:2307.16376 (2023).

3. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

4. Yingpeng Du, Di Luo, Rui Yan, Hongzhi Liu, Yang Song, Hengshu Zhu, and Jie Zhang. 2023. Enhancing job recommendation through llm-based generative adversarial networks. arXiv preprint arXiv:2307.10747 (2023).

5. Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023. Recommender systems in the era of large language models (llms). arXiv preprint arXiv:2307.02046 (2023).

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