AgentDR Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents

• URL: http://arxiv.org/abs/2510.05598v1
• Date: Tue, 07 Oct 2025 05:48:05 GMT
• 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,
• Abstract summary: We propose a novel LLM-agent framework, AgenDR, which bridges LLM reasoning with scalable recommendation tools.<n>Our approach delegates full-ranking tasks to traditional models while utilizing LLMs to integrate multiple recommendation outputs.<n>We show that our framework achieves superior full-ranking performance, yielding on average a twofold improvement over its underlying tools.
• Score: 42.177723613925146
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 enhances recommendation relevance through relational reasoning. Through extensive experiments on three public grocery datasets, we show that our framework achieves superior full-ranking performance, yielding on average a twofold improvement over its underlying tools. We also introduce a new LLM-based evaluation metric that jointly measures semantic alignment and ranking correctness.

Related papers

• LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation [49.78419076215196]
  Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items.<n>Traditional sequential recommenders rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns.<n>Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions.<n>We argue that an ideal embedding model should seamlessly integrate CF signals with rich semantic representations to improve both in-domain and out-of-domain recommendation performance.
  arXiv  Detail & Related papers  (2025-06-16T13:27:06Z)
• What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals [4.297070083645049]
  User-item interactions contain rich collaborative signals that form the backbone of many successful recommender systems.<n>It remains unclear whether large language models (LLMs) can effectively reason over this type of collaborative information.<n>We introduce a simple retrieval-augmented generation (RAG) method that enhances LLMs by grounding their predictions in structured interaction data.
  arXiv  Detail & Related papers  (2025-05-27T05:18:57Z)
• DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation [83.21140655248624]
  Large language models (LLMs) have been introduced into recommender systems (RSs)<n>We propose DeepRec, a novel LLM-based RS that enables autonomous multi-turn interactions between LLMs and TRMs for deep exploration of the item space.<n> Experiments on public datasets demonstrate that DeepRec significantly outperforms both traditional and LLM-based baselines.
  arXiv  Detail & Related papers  (2025-05-22T15:49:38Z)
• Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration [22.650609670923732]
  We propose a parallel recommendation framework that decouples large language models from candidate pre-selection.<n>Our framework connects LLMs and recommendation models in a parallel manner, allowing each component to independently utilize its strengths.
  arXiv  Detail & Related papers  (2025-04-16T09:17:45Z)
• Enhancing High-order Interaction Awareness in LLM-based Recommender Model [3.7623606729515133]
  This paper presents an enhanced LLM-based recommender (ELMRec) We enhance whole-word embeddings to substantially enhance LLMs' interpretation of graph-constructed interactions for recommendations. Our ELMRec outperforms state-of-the-art (SOTA) methods in both direct and sequential recommendations.
  arXiv  Detail & Related papers  (2024-09-30T06:07:12Z)
• LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation [57.49045064294086]
  Large Language Model (LLM) has the ability to capture semantic relationships between items, independent of their popularity.<n>We introduce LLMEmb, a novel method leveraging LLM to generate item embeddings that enhance Sequential Recommender Systems (SRS) performance.
  arXiv  Detail & Related papers  (2024-09-30T03:59:06Z)
• Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation [83.87767101732351]
  Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. We propose DARec, a sequential recommendation model built on top of coarse-grained adaption for capturing inter-item relations.
  arXiv  Detail & Related papers  (2024-08-14T10:03:40Z)
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations [53.76682562935373]
  We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools. InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
  arXiv  Detail & Related papers  (2023-08-31T07:36:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.