用检索增强大模型，让推荐系统更懂用户多维度兴趣。
R3-REC: Reasoning-Driven Recommendation via Retrieval-Augmented LLMs over Multi-Granular Interest Signals
- 通过多粒度兴趣信号检索与推理，统一建模用户长期短期意图。
- 在多个数据集上，最高提升10.2%（HR@1）和6.4%（HR@5）。
- 适合需要理解复杂用户行为的推荐场景，如电商、内容平台。
本文针对序列推荐中的两大难题：（i）证据不足——冷启动稀疏性与噪声多变的物品文本；（ii）动态多面意图建模不透明，跨越长短期视角。我们提出R3-REC（推理-检索-推荐），一种以提示为中心、基于检索增强的框架，整合了多层级用户意图推理、物品语义提取、长短兴趣极性挖掘、相似用户协同增强、基于推理的兴趣匹配与评分。在ML-1M、Games和Bundle数据集上，R3-REC持续超越强基线神经网络与LLM模型，端到端延迟可控，最高实现HR@1提升10.2%、HR@5提升6.4%。消融实验验证各模块互补增益。
原文摘要 · Abstract (English)
This paper addresses two persistent challenges in sequential recommendation: (i) evidence insufficiency-cold-start sparsity together with noisy, length-varying item texts; and (ii) opaque modeling of dynamic, multi-faceted intents across long/short horizons. We propose R3-REC (Reasoning-Retrieval-Recommendation), a prompt-centric, retrieval-augmented framework that unifies Multi-level User Intent Reasoning, Item Semantic Extraction, Long-Short Interest Polarity Mining, Similar User Collaborative Enhancement, and Reasoning-based Interest Matching and Scoring. Across ML-1M, Games, and Bundle, R3-REC consistently surpasses strong neural and LLM baselines, yielding improvements up to +10.2% (HR@1) and +6.4% (HR@5) with manageable end-to-end latency. Ablations corroborate complementary gains of all modules.
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