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[2510.05598] AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
Summary
The paper presents AgentDR, a novel framework that enhances recommendation systems by leveraging LLMs to understand implicit item relationships, improving ranking performance significantly.
Why It Matters
As recommendation systems become increasingly integral to user experience across platforms, improving their accuracy and relevance is crucial. AgentDR addresses common issues like hallucination and ranking inefficiencies, making it a significant advancement in the field of information retrieval and AI-driven recommendations.
Key Takeaways
- AgentDR integrates LLMs to enhance recommendation relevance.
- The framework mitigates hallucination in recommendation outputs.
- It achieves a twofold improvement in full-ranking performance over traditional methods.
- Introduces a new evaluation metric for semantic alignment and ranking correctness.
- Utilizes user history to reason over implicit item relationships.
Computer Science > Information Retrieval arXiv:2510.05598 (cs) [Submitted on 7 Oct 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents Authors:Mingdai Yang, Nurendra Choudhary, Jiangshu Du, Edward W.Huang, Philip S.Yu, Karthik Subbian, Danai Kourta View a PDF of the paper titled AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents, by Mingdai Yang and 6 other authors View PDF HTML (experimental) Abstract:Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-existent items and full-catalog ranking. Besides, a largely underexplored opportunity lies in leveraging LLMs'commonsense reasoning to capture user intent through substitute and complement relationships between items, which are usually implicit in datasets and difficult for traditional ID-based recommenders to capture. In this work, we propose a novel LLM-agent framework, AgenDR, which bridges LLM reasoning with scalable recommendation tools. Our approach delegates full-ranking tasks to traditional models while utilizing LLMs to (i) integrate multiple recommendation outputs based on personalized tool suitability and (ii) reason over substitute and complement relationships grounded in user history. This design mitigates hallucination, scales to large catalogs, and enhanc...
