Research Paper#Recommender Systems, LLMs, Cognitive Architectures🔬 ResearchAnalyzed: Jan 3, 2026 15:54
CogRec: A Cognitive Recommender Agent for Explainable Recommendations
Published:Dec 30, 2025 09:50
•1 min read
•ArXiv
Analysis
This paper addresses the limitations of Large Language Models (LLMs) in recommendation systems by integrating them with the Soar cognitive architecture. The key contribution is the development of CogRec, a system that combines the strengths of LLMs (understanding user preferences) and Soar (structured reasoning and interpretability). This approach aims to overcome the black-box nature, hallucination issues, and limited online learning capabilities of LLMs, leading to more trustworthy and adaptable recommendation systems. The paper's significance lies in its novel approach to explainable AI and its potential to improve recommendation accuracy and address the long-tail problem.
Key Takeaways
- •Combines LLMs and Soar for explainable recommendations.
- •Addresses limitations of LLMs like black-box nature and hallucination.
- •Employs a Perception-Cognition-Action (PCA) cycle.
- •Dynamically queries LLMs for solutions to impasses.
- •Uses Soar's chunking for online learning and rule creation.
- •Demonstrates advantages in accuracy, explainability, and long-tail problem solving.
Reference
“CogRec leverages Soar as its core symbolic reasoning engine and leverages an LLM for knowledge initialization to populate its working memory with production rules.”