用并行架构让大模型直接检索全量商品，提升推荐多样性与效果
Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration
- 大模型生成个性化查询和增强描述，绕过预筛选瓶颈
- 在多个数据集上推荐性能最高提升57%，且更注重新颖性
- 适合想用大模型提升推荐多样性的研究者与工程师
近期研究尝试将大语言模型（LLMs）融入推荐系统，但面临训练偏差和串行架构的瓶颈。为此，我们提出 Query-to-Recommendation 框架，通过并行方式解耦大模型与候选物品预筛选，实现对全量物品池的直接检索。该框架中，大模型生成特征增强的物品描述和个性化用户查询，从而在零样本条件下捕捉多样化偏好并支持丰富的语义匹配。为融合大模型与协同信号的优势，引入自适应重排序策略。大量实验表明，该方法在多个数据集上性能最高提升57%，同时显著提升推荐的新颖性和多样性。
原文摘要 · Abstract (English)
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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