@inproceedings{wang-etal-2024-recmind,
title = "{R}ec{M}ind: Large Language Model Powered Agent For Recommendation",
author = "Wang, Yancheng and
Jiang, Ziyan and
Chen, Zheng and
Yang, Fan and
Zhou, Yingxue and
Cho, Eunah and
Fan, Xing and
Lu, Yanbin and
Huang, Xiaojiang and
Yang, Yingzhen",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.271",
pages = "4351--4364",
abstract = "While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide zero-shot personalized recommendations. We propose a Self-Inspiring algorithm to improve the planning ability. At each intermediate step, the LLM {``}self-inspires{''} to consider all previously explored states to plan for the next step. This mechanism greatly improves the model{'}s ability to comprehend and utilize historical information in planning for recommendation. We evaluate RecMind{'}s performance in various recommendation scenarios. Our experiment shows that RecMind outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5.",
}
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<abstract>While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide zero-shot personalized recommendations. We propose a Self-Inspiring algorithm to improve the planning ability. At each intermediate step, the LLM “self-inspires” to consider all previously explored states to plan for the next step. This mechanism greatly improves the model’s ability to comprehend and utilize historical information in planning for recommendation. We evaluate RecMind’s performance in various recommendation scenarios. Our experiment shows that RecMind outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5.</abstract>
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%0 Conference Proceedings
%T RecMind: Large Language Model Powered Agent For Recommendation
%A Wang, Yancheng
%A Jiang, Ziyan
%A Chen, Zheng
%A Yang, Fan
%A Zhou, Yingxue
%A Cho, Eunah
%A Fan, Xing
%A Lu, Yanbin
%A Huang, Xiaojiang
%A Yang, Yingzhen
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F wang-etal-2024-recmind
%X While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide zero-shot personalized recommendations. We propose a Self-Inspiring algorithm to improve the planning ability. At each intermediate step, the LLM “self-inspires” to consider all previously explored states to plan for the next step. This mechanism greatly improves the model’s ability to comprehend and utilize historical information in planning for recommendation. We evaluate RecMind’s performance in various recommendation scenarios. Our experiment shows that RecMind outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5.
%U https://aclanthology.org/2024.findings-naacl.271
%P 4351-4364
Markdown (Informal)
[RecMind: Large Language Model Powered Agent For Recommendation](https://aclanthology.org/2024.findings-naacl.271) (Wang et al., Findings 2024)
ACL
- Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, and Yingzhen Yang. 2024. RecMind: Large Language Model Powered Agent For Recommendation. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4351–4364, Mexico City, Mexico. Association for Computational Linguistics.