Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Survey on LLM-powered Agents for Recommender Systems

Qiyao Peng, Hongtao Liu, Hua Huang, Qing Yang, Minglai Shao

TL;DR

Traditional recommender systems struggle to capture complex user intents, dynamic interactions, and explainability. The survey categorizes LLM-powered agents into three paradigms—recommender-oriented, interaction-oriented, and simulation-oriented—and presents a unified four-module architecture (Profile, Memory, Planning, Action) to analyze implementations. It catalogs diverse datasets (e.g., Books, Movielens, Steam, Last.fm, Yelp) and conversational resources (ReDial, Reddit, OpenDialKG), and reviews a range of evaluation metrics spanning standard recommendation, language generation, RL, and conversational efficiency, plus custom indicators. The work highlights opportunities and challenges in system architecture, evaluation standardization, and security, offering a roadmap for research and practical deployment of LLM-powered agents in recommender systems.

Abstract

Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainable recommendations. The emergence of Large Language Model (LLM)-powered agents offers a promising approach by enabling natural language interactions and interpretable reasoning, potentially transforming research in recommender systems. This survey provides a systematic review of the emerging applications of LLM-powered agents in recommender systems. We identify and analyze three key paradigms in current research: (1) Recommender-oriented approaches, which leverage intelligent agents to enhance the fundamental recommendation mechanisms; (2) Interaction-oriented approaches, which facilitate dynamic user engagement through natural dialogue and interpretable suggestions; and (3) Simulation-oriented approaches, which employ multi-agent frameworks to model complex user-item interactions and system dynamics. Beyond paradigm categorization, we analyze the architectural foundations of LLM-powered recommendation agents, examining their essential components: profile construction, memory management, strategic planning, and action execution. Our investigation extends to a comprehensive analysis of benchmark datasets and evaluation frameworks in this domain. This systematic examination not only illuminates the current state of LLM-powered agent recommender systems but also charts critical challenges and promising research directions in this transformative field.

A Survey on LLM-powered Agents for Recommender Systems

TL;DR

Traditional recommender systems struggle to capture complex user intents, dynamic interactions, and explainability. The survey categorizes LLM-powered agents into three paradigms—recommender-oriented, interaction-oriented, and simulation-oriented—and presents a unified four-module architecture (Profile, Memory, Planning, Action) to analyze implementations. It catalogs diverse datasets (e.g., Books, Movielens, Steam, Last.fm, Yelp) and conversational resources (ReDial, Reddit, OpenDialKG), and reviews a range of evaluation metrics spanning standard recommendation, language generation, RL, and conversational efficiency, plus custom indicators. The work highlights opportunities and challenges in system architecture, evaluation standardization, and security, offering a roadmap for research and practical deployment of LLM-powered agents in recommender systems.

Abstract

Recommender systems are essential components of many online platforms, yet traditional approaches still struggle with understanding complex user preferences and providing explainable recommendations. The emergence of Large Language Model (LLM)-powered agents offers a promising approach by enabling natural language interactions and interpretable reasoning, potentially transforming research in recommender systems. This survey provides a systematic review of the emerging applications of LLM-powered agents in recommender systems. We identify and analyze three key paradigms in current research: (1) Recommender-oriented approaches, which leverage intelligent agents to enhance the fundamental recommendation mechanisms; (2) Interaction-oriented approaches, which facilitate dynamic user engagement through natural dialogue and interpretable suggestions; and (3) Simulation-oriented approaches, which employ multi-agent frameworks to model complex user-item interactions and system dynamics. Beyond paradigm categorization, we analyze the architectural foundations of LLM-powered recommendation agents, examining their essential components: profile construction, memory management, strategic planning, and action execution. Our investigation extends to a comprehensive analysis of benchmark datasets and evaluation frameworks in this domain. This systematic examination not only illuminates the current state of LLM-powered agent recommender systems but also charts critical challenges and promising research directions in this transformative field.

Paper Structure

This paper contains 26 sections, 2 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Traditional Recommendation
  4. LLM as Agent
  5. LLM Agents for Recommendation
  6. Methods
  7. Method Objective
  8. Agent Components
  9. Datasets and Evaluations
  10. Datasets
  11. Traditional Recommendation Dataset
  12. Conversational Recommendation Dataset
  13. Evaluation
  14. Standard Recommendation Metrics
  15. Language Generation Quality
  16. ...and 11 more sections

Figures (2)

  • Figure 1: Illustration of Different Method Objectives. We classify existing methods into the following three categories: (1) Recommender-oriented method; (2) Interaction-oriented method; (3) Simulation-oriented method.
  • Figure 2: Illustration of Agent Components and Corresponding Functions.