@inproceedings{tsai-etal-2024-leveraging,
title = "Leveraging {LLM} Reasoning Enhances Personalized Recommender Systems",
author = "Tsai, Alicia and
Kraft, Adam and
Jin, Long and
Cai, Chenwei and
Hosseini, Anahita and
Xu, Taibai and
Zhang, Zemin and
Hong, Lichan and
Chi, Ed and
Yi, Xinyang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.780",
pages = "13176--13188",
abstract = "Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. While tasks like arithmetic reasoning involve clear, definitive answers and logical chains of thought, the application of LLM reasoning in recommendation systems (RecSys) presents a distinct challenge. RecSys tasks revolve around subjectivity and personalized preferences, an under-explored domain in utilizing LLMs{'} reasoning capabilities. Our study explores several aspects to better understand reasoning for RecSys and demonstrate how task quality improves by utilizing LLM reasoning for both zero-shot and fine-tuning settings. Additionally, we propose $\textbf{Rec-SAVER}$ ($\textbf{Rec}$ommender $\textbf{S}$ystems $\textbf{A}$utomatic $\textbf{V}$erification and $\textbf{E}$valuation of $\textbf{R}$easoning) to automatically assess the quality of LLM reasoning responses without the requirement of curated gold references or human raters. We show that our framework aligns with real human judgment on the coherence and faithfulness of reasoning responses. Overall, our work shows that incorporating reasoning into RecSys can improve personalized tasks, paving the way for further advancements in recommender system methodologies.",
}
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<abstract>Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. While tasks like arithmetic reasoning involve clear, definitive answers and logical chains of thought, the application of LLM reasoning in recommendation systems (RecSys) presents a distinct challenge. RecSys tasks revolve around subjectivity and personalized preferences, an under-explored domain in utilizing LLMs’ reasoning capabilities. Our study explores several aspects to better understand reasoning for RecSys and demonstrate how task quality improves by utilizing LLM reasoning for both zero-shot and fine-tuning settings. Additionally, we propose Rec-SAVER (Recommender Systems Automatic Verification and Evaluation of Reasoning) to automatically assess the quality of LLM reasoning responses without the requirement of curated gold references or human raters. We show that our framework aligns with real human judgment on the coherence and faithfulness of reasoning responses. Overall, our work shows that incorporating reasoning into RecSys can improve personalized tasks, paving the way for further advancements in recommender system methodologies.</abstract>
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%0 Conference Proceedings
%T Leveraging LLM Reasoning Enhances Personalized Recommender Systems
%A Tsai, Alicia
%A Kraft, Adam
%A Jin, Long
%A Cai, Chenwei
%A Hosseini, Anahita
%A Xu, Taibai
%A Zhang, Zemin
%A Hong, Lichan
%A Chi, Ed
%A Yi, Xinyang
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F tsai-etal-2024-leveraging
%X Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. While tasks like arithmetic reasoning involve clear, definitive answers and logical chains of thought, the application of LLM reasoning in recommendation systems (RecSys) presents a distinct challenge. RecSys tasks revolve around subjectivity and personalized preferences, an under-explored domain in utilizing LLMs’ reasoning capabilities. Our study explores several aspects to better understand reasoning for RecSys and demonstrate how task quality improves by utilizing LLM reasoning for both zero-shot and fine-tuning settings. Additionally, we propose Rec-SAVER (Recommender Systems Automatic Verification and Evaluation of Reasoning) to automatically assess the quality of LLM reasoning responses without the requirement of curated gold references or human raters. We show that our framework aligns with real human judgment on the coherence and faithfulness of reasoning responses. Overall, our work shows that incorporating reasoning into RecSys can improve personalized tasks, paving the way for further advancements in recommender system methodologies.
%U https://aclanthology.org/2024.findings-acl.780
%P 13176-13188
Markdown (Informal)
[Leveraging LLM Reasoning Enhances Personalized Recommender Systems](https://aclanthology.org/2024.findings-acl.780) (Tsai et al., Findings 2024)
ACL
- Alicia Tsai, Adam Kraft, Long Jin, Chenwei Cai, Anahita Hosseini, Taibai Xu, Zemin Zhang, Lichan Hong, Ed Chi, and Xinyang Yi. 2024. Leveraging LLM Reasoning Enhances Personalized Recommender Systems. In Findings of the Association for Computational Linguistics ACL 2024, pages 13176–13188, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.