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.

翻译：本文致力于解决序列推荐中的两个长期挑战：（i）证据不足——冷启动稀疏性与噪声大、长度不一的物品文本并存；（ii）对长/短期动态多层面用户意图的建模过程不透明。我们提出了R3-REC（推理-检索-推荐），一个以提示为中心、检索增强的框架，它统一了多级用户意图推理、物品语义提取、长短兴趣极性挖掘、相似用户协同增强以及基于推理的兴趣匹配与评分。在ML-1M、Games和Bundle数据集上的实验表明，R3-REC持续超越强大的神经模型与大语言模型基线，在可控的端到端延迟内，实现了最高+10.2%（HR@1）和+6.4%（HR@5）的性能提升。消融研究证实了所有模块的互补增益。