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arXiv · 2502.13845

Improving LLM-powered Recommendations with Personalized Information

Jiahao Liu·Xueshuo Yan·Dongsheng Li·Guangping Zhang·Hansu Gu·Peng Zhang·Tun Lu·Li Shang·Ning Gu
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Abstract

Due to the lack of explicit reasoning modeling, existing LLM-powered recommendations fail to leverage LLMs' reasoning capabilities effectively. In this paper, we propose a pipeline called CoT-Rec, which integrates two key Chain-of-Thought (CoT) processes -- user preference analysis and item perception analysis -- into LLM-powered recommendations, thereby enhancing the utilization of LLMs' reasoning abilities. CoT-Rec consists of two stages: (1) personalized information extraction, where user preferences and item perception are extracted, and (2) personalized information utilization, where this information is incorporated into the LLM-powered recommendation process. Experimental results demonstrate that CoT-Rec shows potential for improving LLM-powered recommendations. The implementation is publicly available at https://github.com/jhliu0807/CoT-Rec.

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Jiahao Liu, Xueshuo Yan, Dongsheng Li, Guangping Zhang, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu. 2025-02-19. Improving LLM-powered Recommendations with Personalized Information. https://arxiv.org/abs/2502.13845

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