Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising results. However, the challenges of deploying LLM4Rec in real-world scenarios remain largely unexplored. In particular, recommender models need incremental adaptation to evolving user preferences, while the suitability of traditional incremental learning methods within LLM4Rec remains ambiguous due to the unique characteristics of Large Language Models (LLMs).
Authors
- Chong Chen (ORCID: https://orcid.org/0000-0003-1417-2295)
- Xiangnan He (ORCID: https://orcid.org/0000-0001-8472-7992)
- Fuli Feng (ORCID: https://orcid.org/0000-0002-5828-9842)
- Tianhao Shi (ORCID: https://orcid.org/0009-0007-9253-3443)
- Qi Tian (ORCID: https://orcid.org/0000-0002-7252-5047)
- Yang Zhang (ORCID: https://orcid.org/0000-0002-7863-5183)
- Zhijian Xu
Institutions
- University of Science and Technology of China (CN)
- National University of Singapore (SG)
- Huawei Technologies (China) (CN)
Publication Details
- Published
- 2024-10-20
- DOI
- https://doi.org/10.1145/3627673.3679922
- Citations
- 4
- Primary Topic
- Recommender Systems and Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.12