Enhancing LLM-Based Recommendations Through Personalized Reasoning

• URL: http://arxiv.org/abs/2502.13845v1
• Date: Wed, 19 Feb 2025 16:08:17 GMT
• Title: Enhancing LLM-Based Recommendations Through Personalized Reasoning
• Authors: Jiahao Liu, Xueshuo Yan, Dongsheng Li, Guangping Zhang, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu,
• Abstract summary: CoT-Rec is a framework that integrates Chain-of-Thought (CoT) reasoning into large language models (LLMs)-driven recommendations. Our experimental analysis demonstrates that CoT-Rec improves recommendation accuracy by making better use of LLMs' reasoning potential.
• Score: 29.393390011083895
• License:
• Abstract: Current recommendation systems powered by large language models (LLMs) often underutilize their reasoning capabilities due to a lack of explicit logical structuring. To address this limitation, we introduce CoT-Rec, a framework that integrates Chain-of-Thought (CoT) reasoning into LLM-driven recommendations by incorporating two crucial processes: user preference analysis and item perception evaluation. CoT-Rec operates in two key phases: (1) personalized data extraction, where user preferences and item perceptions are identified, and (2) personalized data application, where this information is leveraged to refine recommendations. Our experimental analysis demonstrates that CoT-Rec improves recommendation accuracy by making better use of LLMs' reasoning potential. The implementation is publicly available at https://anonymous.4open.science/r/CoT-Rec.

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