CogRec: A Cognitive Recommender Agent Fusing Large Language Models and Soar for Explainable Recommendation

TL;DR

CogRec is a novel cognitive recommender agent that combines Large Language Models and the Soar architecture to provide accurate, explainable, and adaptable recommendations through a dynamic reasoning and learning cycle.

Contribution

This paper introduces CogRec, a hybrid system that integrates LLMs with Soar for improved explainability and online learning in recommendation systems.

Findings

CogRec outperforms baseline models in recommendation accuracy.

It provides highly interpretable rationales for its suggestions.

Demonstrates effectiveness in addressing the long-tail problem.

Abstract

Large Language Models (LLMs) have demonstrated a remarkable capacity in understanding user preferences for recommendation systems. However, they are constrained by several critical challenges, including their inherent "Black-Box" characteristics, susceptibility to knowledge hallucination, and limited online learning capacity. These factors compromise their trustworthiness and adaptability. Conversely, cognitive architectures such as Soar offer structured and interpretable reasoning processes, yet their knowledge acquisition is notoriously laborious. To address these complementary challenges, we propose a novel cognitive recommender agent called CogRec which synergizes the strengths of LLMs with the Soar cognitive architecture. CogRec leverages Soar as its core symbolic reasoning engine and leverages an LLM for knowledge initialization to populate its working memory with production rules. The agent operates on a Perception-Cognition-Action(PCA) cycle. Upon encountering an impasse, it dynamically queries the LLM to obtain a reasoned solution. This solution is subsequently transformed into a new symbolic production rule via Soar's chunking mechanism, thereby enabling robust online learning. This learning paradigm allows the agent to continuously evolve its knowledge base and furnish highly interpretable rationales for its recommendations. Extensive evaluations conducted on three public datasets demonstrate that CogRec demonstrates significant advantages in recommendation accuracy, explainability, and its efficacy in addressing the long-tail problem.