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arXiv · 2504.03965

Automating Personalization: Prompt Optimization for Recommendation Reranking

Chen Wang·Mingdai Yang·Zhiwei Liu·Pan Li·Linsey Pang·Qingsong Wen·Philip Yu
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Abstract

Modern recommender systems increasingly leverage large language models (LLMs) for reranking to improve personalization. However, existing approaches face two key limitations: (1) heavy reliance on manually crafted prompts that are difficult to scale, and (2) inadequate handling of unstructured item metadata that complicates preference inference. We present AGP (Auto-Guided Prompt Refinement), a novel framework that automatically optimizes user profile generation prompts for personalized reranking. AGP introduces two key innovations: (1) position-aware feedback mechanisms for precise ranking correction, and (2) batched training with aggregated feedback to enhance generalization.

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Chen Wang, Mingdai Yang, Zhiwei Liu, Pan Li, Linsey Pang, Qingsong Wen, Philip Yu. 2025-04-04. Automating Personalization: Prompt Optimization for Recommendation Reranking. https://arxiv.org/abs/2504.03965

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