@inproceedings{bao-etal-2025-customizing,
title = "Customizing In-context Learning for Dynamic Interest Adaption in {LLM}-based Recommendation",
author = "Bao, Keqin and
Yan, Ming and
Zhang, Yang and
Zhang, Jizhi and
Wang, Wenjie and
Feng, Fuli and
He, Xiangnan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.735/",
doi = "10.18653/v1/2025.findings-acl.735",
pages = "14278--14291",
ISBN = "979-8-89176-256-5",
abstract = "Frequently updating Large Language Model (LLM)-based recommender systems to adapt to dynamic user interests{---}as done for traditional ones{---}is impractical due to high training costs, even with acceleration methods. This work explores the possibility of adapting the model to dynamic user interests without any model-level updates via In-context Learning (ICL), which enables adaptation through few-shot examples within input prompts. While using recent user interactions as ICL demonstrations offers a potential solution for dynamic interest adaptation, existing LLM-based recommenders face critical limitations: recommendation-specific tuning often diminishes the model{'}s in-context learning ability, and the original LLM{'}s ICL lacks task-specific optimization for recommendations. To bridge this gap, we introduce RecICL, a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats. RecICL achieves dual objectives: (1) preserving fundamental ICL capabilities during recommendation adaptation and (2) dynamically capturing user preference evolution through the most recent interactions. Extensive experiments across multiple benchmarks demonstrate RecICL{'}s superior performance, achieving better results without model updates. Our implementation is publicly available at \url{https://anonymous.4open.science/r/RecICL-8003}."
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<abstract>Frequently updating Large Language Model (LLM)-based recommender systems to adapt to dynamic user interests—as done for traditional ones—is impractical due to high training costs, even with acceleration methods. This work explores the possibility of adapting the model to dynamic user interests without any model-level updates via In-context Learning (ICL), which enables adaptation through few-shot examples within input prompts. While using recent user interactions as ICL demonstrations offers a potential solution for dynamic interest adaptation, existing LLM-based recommenders face critical limitations: recommendation-specific tuning often diminishes the model’s in-context learning ability, and the original LLM’s ICL lacks task-specific optimization for recommendations. To bridge this gap, we introduce RecICL, a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats. RecICL achieves dual objectives: (1) preserving fundamental ICL capabilities during recommendation adaptation and (2) dynamically capturing user preference evolution through the most recent interactions. Extensive experiments across multiple benchmarks demonstrate RecICL’s superior performance, achieving better results without model updates. Our implementation is publicly available at https://anonymous.4open.science/r/RecICL-8003.</abstract>
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%0 Conference Proceedings
%T Customizing In-context Learning for Dynamic Interest Adaption in LLM-based Recommendation
%A Bao, Keqin
%A Yan, Ming
%A Zhang, Yang
%A Zhang, Jizhi
%A Wang, Wenjie
%A Feng, Fuli
%A He, Xiangnan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F bao-etal-2025-customizing
%X Frequently updating Large Language Model (LLM)-based recommender systems to adapt to dynamic user interests—as done for traditional ones—is impractical due to high training costs, even with acceleration methods. This work explores the possibility of adapting the model to dynamic user interests without any model-level updates via In-context Learning (ICL), which enables adaptation through few-shot examples within input prompts. While using recent user interactions as ICL demonstrations offers a potential solution for dynamic interest adaptation, existing LLM-based recommenders face critical limitations: recommendation-specific tuning often diminishes the model’s in-context learning ability, and the original LLM’s ICL lacks task-specific optimization for recommendations. To bridge this gap, we introduce RecICL, a framework that establishes recommendation-oriented in-context learning by structuring recent user interactions and current inputs into ICL formats. RecICL achieves dual objectives: (1) preserving fundamental ICL capabilities during recommendation adaptation and (2) dynamically capturing user preference evolution through the most recent interactions. Extensive experiments across multiple benchmarks demonstrate RecICL’s superior performance, achieving better results without model updates. Our implementation is publicly available at https://anonymous.4open.science/r/RecICL-8003.
%R 10.18653/v1/2025.findings-acl.735
%U https://aclanthology.org/2025.findings-acl.735/
%U https://doi.org/10.18653/v1/2025.findings-acl.735
%P 14278-14291
Markdown (Informal)
[Customizing In-context Learning for Dynamic Interest Adaption in LLM-based Recommendation](https://aclanthology.org/2025.findings-acl.735/) (Bao et al., Findings 2025)
ACL