AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
This addresses the need for explainable and efficient recommender systems for users and developers, representing an incremental advancement by combining existing techniques like tool-augmented models and retrieval-augmented generation.
The paper tackled the problem of opaque reasoning and knowledge constraints in recommender systems using foundation models, introducing AgenticRAG, a framework that achieved improvements in NDCG@10 of 0.4% on Amazon Electronics, 0.8% on MovieLens-1M, and 1.6% on Yelp datasets.
Foundation models have revolutionized artificial intelligence, yet their application in recommender systems remains limited by reasoning opacity and knowledge constraints. This paper introduces AgenticRAG, a novel framework that combines tool-augmented foundation models with retrieval-augmented generation for zero-shot explainable recommendations. Our approach integrates external tool invocation, knowledge retrieval, and chain-of-thought reasoning to create autonomous recommendation agents capable of transparent decision-making without task-specific training. Experimental results on three real-world datasets demonstrate that AgenticRAG achieves consistent improvements over state-of-the-art baselines, with NDCG@10 improvements of 0.4\% on Amazon Electronics, 0.8\% on MovieLens-1M, and 1.6\% on Yelp datasets. The framework exhibits superior explainability while maintaining computational efficiency comparable to traditional methods.