Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation

TL;DR

RGCF-XRec tackles the gap between collaborative filtering signals and explainable language-model recommendations by embedding reasoning-guided CF knowledge into a unified, multi-signal representation fed to a frozen LLM. The method assembles a three-layer architecture (Representation, Fusion/Projection, Generation) that aligns collaborative priors with semantic item descriptions and offline chain-of-thought reasoning, enabling single-pass next-item prediction plus personalized explanations. It introduces a four-dimension CoT scoring mechanism to filter high-quality reasoning traces and employs a lightweight LoRA-tuned LLaMA backbone for efficiency. Empirically, RGCF-XRec improves ranking and explanation metrics across Amazon Sports, Toys, and Beauty, reduces cold-warm gaps, and demonstrates robust zero-shot generalization, all while maintaining scalable training using a 3B-scale LLM. The work offers a principled foundation for trustworthy, explainable recommendations by integrating reasoning traces directly into the model’s decision process and prompts.

Abstract

Large Language Models (LLMs) exhibit potential for explainable recommendation systems but overlook collaborative signals, while prevailing methods treat recommendation and explanation as separate tasks, resulting in a memory footprint. We present RGCF-XRec, a hybrid framework that introduces reasoning-guided collaborative filtering (CF) knowledge into a language model to deliver explainable sequential recommendations in a single step. Theoretical grounding and empirical findings reveal that RGCF-XRec offers three key merits over leading CF-aware LLM-based methods: (1) reasoning-guided augmentation of CF knowledge through contextual prompting to discover latent preferences and interpretable reasoning paths; (2) an efficient scoring mechanism based on four dimensions: coherence, completeness, relevance, and consistency to mitigate noisy CF reasoning traces and retain high-quality explanations; (3) a unified representation learning network that encodes collaborative and semantic signals, enabling a structured prompt to condition the LLM for explainable sequential recommendation. RGCF-XRec demonstrates consistent improvements across Amazon datasets, Sports, Toys, and Beauty, comprising 642,503 user-item interactions. It improves HR@10 by 7.38\% in Sports and 4.59\% in Toys, along with ROUGE-L by 8.02\% and 3.49\%, respectively. It reduces the cold warm performance gap, achieving overall gains of 14.5\% in cold-start and 11.9\% in warm start scenarios, and enhances zero-shot HR@5 by 18.54\% in Beauty and 23.16\% in Toys, highlighting effective generalization and robustness. Moreover, RGCF-XRec achieves training efficiency with a lightweight LLaMA 3.2-3B backbone, ensuring scalability for real-world applications.

Figures (7)

• Figure 1: Overall workflow of the proposed RGCF-XRec across three conceptual layers. In the Representation layer, frozen CF and text-encoding models extract behavioral and semantic knowledge (CF Enhancement). The Fusion and Projection layer trains a Unified Projection Network that aligns collaborative and semantic item spaces using modality-specific encoders/decoders and employs three projection MLPs to transform the user representation, unified item embedding, and offline, quality-filtered in-context CoT into LLM tokens for prompt construction. Finally, the Generation layer uses a frozen LLM to perform next-item prediction and explanation generation jointly.
• Figure 2: Detailed architecture of RGCF-XRec. (a) In-context CoT Reasoning: A LoRA-tuned LLaMA-R$^{2}$ model generates CoT reasoning traces from user–item interactions and item metadata, which are quality-scored on coherence, completeness, relevance, and consistency. Only high-quality CoTs are retained for downstream use. (b) Recommendation Generation: A Unified Projection Network aligns collaborative and semantic item spaces through modality-specific encoders and decoders, optimizing alignment, reconstruction, and recommendation losses to learn unified item embeddings. Three projection MLPs transform the user representation, unified item embedding, and retained CoT into the LLM token space for prompt construction. Finally, a frozen LLM jointly generates the next-item prediction and its conditioned explanation in a single decoding pass.
• Figure 3: Performance analysis of RGCF-XRec against four baseline models in both warm-start and cold-start item recommendation scenarios.
• Figure 4: Zero-shot learning results of RGCF-XRec in comparison with four baselines on the Toys and Beauty dataset.
• Figure 5: CoT Scoring Threshold Sensitivity on Toys and Sports datasets.