R3-REC: Reasoning-Driven Recommendation via Retrieval-Augmented LLMs over Multi-Granular Interest Signals

Yuchen Miao; Mingxuan Cui; Yitong Zhu; Yu Wang; Siyang Xu

cs.IRMar 14, 2026

R3-REC: Reasoning-Driven Recommendation via Retrieval-Augmented LLMs over Multi-Granular Interest Signals

Authors: Yuchen Miao, Mingxuan Cui, Yitong Zhu, Yu Wang, Siyang Xu

Organizations: Sydney Smart Technology College, Northeastern University, China

Abstract

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.

Figures & tables

Figure 1: Challenges and solution: (i) evidence insufficiency (cold-start + noisy item texts), (ii) opaque modeling of long/short-horizon intents.

Figure 2: Methodology Overview of $R^{3}$ -REC. The pipeline consists of four stages: (1) Raw Data Processing handles user session streams; (2) Multi-granularity Enhancement distills item semantics via LLMs and models hierarchical user interests (including intent reasoning and long-short term polarity mining); (3) Prompt-guided Assembly Reasoning constructs a comprehensive prompt context by inserting retrieved similar users, inferred intents, and candidates; (4) Context-aware Recommendation employs an LLM to perform feature analysis, generating both ranking scores and interpretative reasoning.

		Conventional Methods		Single-Intent Methods			Multi-Intent Methods			LLM Methods				Ours	Improve	$p$ -value
Data	Metrics	MostPop	FPMC	NARM	STAMP	GCE-GNN	MCPRN	HIDE	Atten-Mixer	NIR	PO4ISR	RecMind	UniRec	R 3 -REC
ML-1M	HR@1	0.0004	0.1132	0.1692 ⋆	0.1584	0.1312	0.1434	0.1498	0.1490	0.0572	0.2000	0.2700 ∗	0.2650	0.287	+6.3%	$4.3\mathrm{e}{-3}$
	HR@5	0.0070	0.3748	0.5230 ⋆	0.5078	0.4748	0.4788	0.4998	0.4932	0.2326	0.5510	0.5720	0.5910 ∗	0.597	+1.0%	$5.9\mathrm{e}{-2}$
	HR@10	0.0120	0.5150	0.7050 ⋆	0.6920	0.6600	0.6640	0.6820	0.6760	0.3550	0.7240	0.7480	0.7620 ∗	0.769	+0.9%	$4.6\mathrm{e}{-2}$
Games	HR@1	–	0.0498	0.0572	0.0556	0.0692	0.0522	0.0696	0.0530	0.1168 ⋆	0.2588	0.3450 ∗	0.3400	0.379	+9.9%	$6.4\mathrm{e}{-5}$
	HR@5	–	0.2564	0.2574	0.2586	0.2744	0.2416	0.2694	0.2472	0.3406 ⋆	0.5866	0.6550 ∗	0.6450	0.679	+3.7%	$3.5\mathrm{e}{-5}$

Table 1: Main comparison on ML-1M , Games , and Bundle . Higher is better. Best results are in bold ; second-best are marked with ∗ .

Figure 3: Ablation on R 3 -REC (HR@1 / HR@5 / NDCG@5). Lower bars on variants indicate performance drops relative to the full model.

Panel of Figure 4 — Figure 4: Hyperparameter sensitivity under two factorizations.

Model	Avg. Tokens		Latency	Est. Cost	HR@1
Model	Input	Output	(ms)	($)	(Games)
NIR [ 21 ]	150	10	250	$<$ 1e-4	0.1168
PO4ISR [ 19 ]	350	15	380	2e-4	0.2588
RecMind [ 23 ]	600	80	920	4e-4	0.3450
R 3 -REC	1250	10	580	6e-4	0.3790

Table 2: Efficiency Analysis (20-item slate). Est. Cost : Based on GPT-3.5 pricing. Latency : Ranking phase only.

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Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task. Previous work has improved personalization by incorporating collaborative and sequential signals through input conditioning or LLM fine-tuning. However, existing approaches often rely on one or more of the following: LLM fine-tuning, additional architectural modules, representation distillation, or item-level conditioning over long interaction histories, increasing training complexity and deployment cost. We propose REPREC, a lightweight framework that reformulates LLM-based sequential recommendation through lightweight user representation alignment. REPREC maps a fixed-size user embedding from a frozen sequential encoder into a small set of learned soft tokens through a lightweight MLP injector that conditions a frozen LLM, leaving both pretrained backbones unchanged while training only the injector. We conducted exhaustive experiments on multiple benchmark datasets and demonstrate that REPREC consistently outperforms LoRA while remaining compatible with different pretrained sequential encoders and LLM backbones, enabling a modular and production-friendly recommendation pipeline without modifying either pretrained component. The gains are particularly pronounced for casual and core users across all datasets, highlighting REPREC's effectiveness in low-data regimes. Finally, when trained on short prompt histories and evaluated with longer contexts, REPREC maintains 85-100% of LoRA's performance while reducing per-epoch training time by an average of 1.51X, demonstrating an effective balance between recommendation quality and computational efficiency for production deployment. The code is available at https://github.com/phdbotcode/REPREC

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