Data-efficient Fine-tuning for LLM-based Recommendation-Reference-Cited by-同舟云学术

Data-efficient Fine-tuning for LLM-based Recommendation

Published:2024-07-10 Issue: Volume:33 Page:365-374
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Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Lin Xinyu¹^ORCID,Wang Wenjie¹^ORCID,Li Yongqi²^ORCID,Yang Shuo³^ORCID,Feng Fuli⁴^ORCID,Wei Yinwei⁵^ORCID,Chua Tat-Seng¹^ORCID

Affiliation:

1. National University of Singapore, Singapore, Singapore

2. The Hong Kong Polytechnic University, Hong Kong SAR, China

3. The University of Hong Kong, Hong Kong SAR, China

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

5. Monash University, Melbourne, Australia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657807

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3. Keqin Bao Jizhi Zhang Wenjie Wang Yang Zhang Zhengyi Yang Yancheng Luo Fuli Feng Xiangnaan He and Qi Tian. 2023 a. A bi-step grounding paradigm for large language models in recommendation systems. arXiv:2308.08434.

4. Keqin Bao Jizhi Zhang Yang Zhang Wenjie Wang Fuli Feng and Xiangnan He. 2023 b. Tallrec: An effective and efficient tuning framework to align large language model with recommendation. In RecSys. ACM.

5. Coresets via bilevel optimization for continual learning and streaming;Borsos Zalán;NeurIPS,2020

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