Large Language Models for Recommendation: Progresses and Future Directions

Author:

Zhang Jizhi1ORCID,Bao Keqin1ORCID,Zhang Yang1ORCID,Wang Wenjie2ORCID,Feng Fuli1ORCID,He Xiangnan1ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, China

2. National University of Singapore, Singapore, Singapore

Publisher

ACM

Reference74 articles.

1. Information Retrieval meets Large Language Models: A strategic report from Chinese IR community

2. Rohan Anil et al. 2023. Palm 2 technical report. arXiv preprint arXiv:2305.10403 (2023).

3. Yuntao Bai et al. 2022a. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073 (2022).

4. Yuntao Bai et al. 2022b. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 (2022).

5. Keqin Bao et al. 2023 a. A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems. CoRR, Vol. abs/2308.08434 (2023). showeprint[arXiv]2308.08434

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1. Large Language Models for Recommendation: Past, Present, and Future;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

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