Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation

2026-02-05

AI Summary

This work addresses the limitations of existing large language model (LLM)-based recommender systems, which often neglect collaborative filtering signals and decouple recommendation from explanation, resulting in high memory overhead and weak interpretability. To overcome these issues, the authors propose RGCF-XRec, a framework that integrates collaborative knowledge into a lightweight LLaMA-3.2-3B model via structured reasoning-guided prompts, enabling one-step interpretable sequential recommendation. Key innovations include a context-enhanced collaborative prompting mechanism, a four-dimensional (coherence, completeness, relevance, consistency) explanation quality evaluator to filter noisy reasoning paths, and a unified representation network that fuses collaborative and semantic signals. Experiments on multiple Amazon datasets demonstrate significant performance gains, with up to 7.38% improvement in HR@10 and 8.02% in ROUGE-L, along with notable enhancements of 14.5% and 23.16% in cold-start and zero-shot scenarios, respectively.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• explainable recommendation
• collaborative filtering
• large language models
• sequential recommendation
• cold start

Innovation

• Reasoning-guided Collaborative Filtering
• Explainable Recommendation
• Large Language Models
• Unified Representation Learning
• Contextual Prompting