2025/04/16 by Donghee Han, Hwanjun Song, Han, Donghee +3 · 4 citations
Computer Science · #Computation and Language (cs.CL) #Digital Rights Management and Security #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Mathematics, Computing, and Information Processing #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2504.11889
openalex publication_date 2025/04/16 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28
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.