@inproceedings{jiang-etal-2025-reclm,
title = "{R}ec{LM}: Recommendation Instruction Tuning",
author = "Jiang, Yangqin and
Yang, Yuhao and
Xia, Lianghao and
Luo, Da and
Lin, Kangyi and
Huang, Chao",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.751/",
doi = "10.18653/v1/2025.acl-long.751",
pages = "15443--15459",
ISBN = "979-8-89176-251-0",
abstract = "Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Networks (GNNs) excel at capturing user-item relationships, their effectiveness is limited when handling sparse data or zero-shot scenarios, primarily due to constraints in ID-based embedding functions. To address these challenges, we propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly integrates large language models with collaborative filtering. Our proposed Recommendation Language Model (RecLM) enhances the capture of user preference diversity through a carefully designed reinforcement learning reward function that facilitates self-augmentation of language models. Comprehensive evaluations demonstrate significant advantages of our approach across various settings, and its plug-and-play compatibility with state-of-the-art recommender systems results in notable performance enhancements."
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%0 Conference Proceedings
%T RecLM: Recommendation Instruction Tuning
%A Jiang, Yangqin
%A Yang, Yuhao
%A Xia, Lianghao
%A Luo, Da
%A Lin, Kangyi
%A Huang, Chao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F jiang-etal-2025-reclm
%X Modern recommender systems aim to deeply understand users’ complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Networks (GNNs) excel at capturing user-item relationships, their effectiveness is limited when handling sparse data or zero-shot scenarios, primarily due to constraints in ID-based embedding functions. To address these challenges, we propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly integrates large language models with collaborative filtering. Our proposed Recommendation Language Model (RecLM) enhances the capture of user preference diversity through a carefully designed reinforcement learning reward function that facilitates self-augmentation of language models. Comprehensive evaluations demonstrate significant advantages of our approach across various settings, and its plug-and-play compatibility with state-of-the-art recommender systems results in notable performance enhancements.
%R 10.18653/v1/2025.acl-long.751
%U https://aclanthology.org/2025.acl-long.751/
%U https://doi.org/10.18653/v1/2025.acl-long.751
%P 15443-15459
Markdown (Informal)
[RecLM: Recommendation Instruction Tuning](https://aclanthology.org/2025.acl-long.751/) (Jiang et al., ACL 2025)
ACL
- Yangqin Jiang, Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, and Chao Huang. 2025. RecLM: Recommendation Instruction Tuning. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15443–15459, Vienna, Austria. Association for Computational Linguistics.