For cost-efficient LLMs, rephrasing, step-back, and structured reasoning prompts raise ranking accuracy; for high-performance LLMs, a simple baseline prompt matches complex prompts at a fraction of the cost.
Improving LLM-powered Recommendations with Personalized Information
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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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Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
For cost-efficient LLMs, rephrasing, step-back, and structured reasoning prompts raise ranking accuracy; for high-performance LLMs, a simple baseline prompt matches complex prompts at a fraction of the cost.