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.
@article{arxiv.2502.13845,
title = {Improving LLM-powered Recommendations with Personalized Information},
author = {Jiahao Liu and Xueshuo Yan and Dongsheng Li and Guangping Zhang and Hansu Gu and Peng Zhang and Tun Lu and Li Shang and Ning Gu},
journal= {arXiv preprint arXiv:2502.13845},
year = {2025}
}