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arXiv · 2504.11889

Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration

Donghee Han·Hwanjun Song·Mun Yong Yi
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Abstract

Recent studies have explored integrating large language models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from serialized architecture. To effectively address these issues, we propose a Query-toRecommendation, a parallel recommendation framework that decouples LLMs from candidate pre-selection and instead enables direct retrieval over the entire item pool. Our framework connects LLMs and recommendation models in a parallel manner, allowing each component to independently utilize its strengths without interfering with the other. In this framework, LLMs are utilized to generate feature-enriched item descriptions and personalized user queries, allowing for capturing diverse preferences and enabling rich semantic matching in a zero-shot manner. To effectively combine the complementary strengths of LLM and collaborative signals, we introduce an adaptive reranking strategy. Extensive experiments demonstrate an improvement in performance up to 57%, while also improving the novelty and diversity of recommendations.

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Donghee Han, Hwanjun Song, Mun Yong Yi. 2025-04-16. Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration. https://arxiv.org/abs/2504.11889

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