An index for papers on large language model agents for recommendation and search.
Please find more details in our survey paper: A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval.
Please cite our survey paper if you find this index helpful.
@article{zhang2025survey,
title={A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval},
author={Zhang, Yu and Qiao, Shutong and Zhang, Jiaqi and Lin, Tzu-Heng and Gao, Chen and Li, Yong},
journal={arXiv preprint arXiv:2503.05659},
year={2025}
}
| Domain | Paper | What agents can do (ability) |
|---|---|---|
| Interaction | RAH! RecSys--Assistant--Human: A Human-Centered Recommendation Framework With LLM Agents [paper] | Assists users in receiving customized recommendations and provide feedback |
| Interaction | Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning [paper] | Uses tools for specific recommendation tasks |
| Interaction | RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems [paper] | Utilizes LLMs as an interface for traditional recommendation tools |
| Interaction | Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs [paper] | Conducts real conversations with users |
| Interaction | FLOW: A Feedback LOop FrameWork for Simultaneously Enhancing Recommendation and User Agents [paper] | Introduces a feedback loop to enable collaboration between the recommendation agent and the user agent |
| Representation | Agentcf: Collaborative learning with autonomous language agents for recommender systems [paper] | Facilitates collaborative learning between user and item agents |
| Representation | Prospect Personalized Recommendation on Large Language Model-based Agent Platform [paper] | Controls the collaboration between the Intelligent Agent items and the Agent Recommenders |
| Representation | KGLA: Knowledge Graph Enhanced Language Agents for Recommendation [paper] | Improves user agent memory |
| System | Recmind: Large language model powered agent for recommendation [paper] | Introduces a self-inspiring algorithm for decision-making |
| System | Recommender ai agent: Integrating large language models for interactive recommendations [paper] | Integrates LLMs and RSs for interactive recommendations |
| System | Multi-Agent Collaboration Framework for Recommender Systems [paper] | Develops a multi-agent collaboration framework for RSs |
| System | Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning [paper] | Emphasizes long-term user retention using LLM-planned RL algorithms |
| System | A multi-agent conversational recommender system [paper] | Tackles dialog control and user feedback integration with multi-agent framework |
| System | Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems [paper] | Uses multimodal, autonomous, multi-agent systems |
| System | Lending interaction wings to recommender systems with conversational agents [paper] | Combines conversational agents and RSs for better interaction |
| System | A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerce [paper] | Combines LLM agent and search engine to optimize conversational recommendation |
| Simulation | On Generative Agents in Recommendation [paper] | Trains LLM agents to simulate real users for evaluation |
| Simulation | RecAgent: A Novel Simulation Paradigm for Recommender Systems [paper] | Simulates user behaviors related to the RS |
| Simulation | Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation [paper] | Uses LLMs to simulate users for conversational recommendation tasks |
| Simulation | SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems [paper] | Develops an RL environment using LLM to simulate user feedback |
| Simulation | A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems [paper] | Proposes a framework for LLM-based user simulators in conversational RSs |
| Simulation | Rethinking the evaluation for conversational recommendation in the era of large language models [paper] | Suggests new evaluation methods using LLMs |
| Simulation | How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation [paper] | Examines reliability and limitations of current LLM-based simulators |
| Simulation | Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground [paper] | Develops an LLM-based eyewear system with conversational common ground |
| Simulation | CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent [paper] | Uses LLM agent to attack LLM-driven RSs |
| Simulation | LLM-Powered User Simulator for Recommender System [paper] | Improves the training efficiency and effectiveness of RSs based on reinforcement learning |
| Role of agent | Paper | What agents can do (ability) |
|---|---|---|
| Decomposer | Laser: Llm agent with state-space exploration for web navigation [paper] | Uses state-space exploration for web navigation tasks |
| Decomposer | Knowagent: Knowledge-augmented planning for llm-based agents [paper] | Integrates knowledge base for task decomposition and logical action execution |
| Decomposer | On the Multi-turn Instruction Following for Conversational Web Agents [paper] | Utilizes self-reflection memory enhancement planning for web navigation tasks |
| Decomposer | A real-world webagent with planning, long context understanding, and program synthesis [paper] | Learns from experience to complete tasks and divide complex instructions |
| Decomposer | Step: Stacked llm policies for web actions [paper] | Introduces dynamic strategy combination through task decomposition |
| Decomposer | Tree Search for Language Model Agents [paper] | Enhances web navigation using tree search algorithms |
| Decomposer | Agent q: Advanced reasoning and learning for autonomous ai agents [paper] | Integrates MCTS-guided search with self-critique for multi-step reasoning |
| Rewriter | CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models [paper] | Enables collaborative search through plug-ins that understand and refine queries |
| Rewriter | Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search [paper] | Assists in constructing personalized dialogue datasets to enhance query quality |
| Rewriter | Trec ikat 2023: The interactive knowledge assistance track overview [paper] | Utilizes internal knowledge of LLMs for better retrieval and response generation |
| Rewriter | LLM Agents Improve Semantic Code Search [paper] | Proposes RAG-powered agents with multi-stream ensemble for semantic code search |
| Executor | AvaTaR: Optimizing LLM Agents for Tool-Assisted Knowledge Retrieval [paper] | Utilizes a comparator LLM to teach the agent how to use tools |
| Executor | Easytool: Enhancing llm-based agents with concise tool instruction [paper] | extracts key information from tool documentation and designs a unified interface |
| Executor | Executable code actions elicit better llm agents [paper] | Integrates LLm agents with a Python interpreter in order to execute code actions |
| Executor | CodeNav: Beyond tool-use to using real-world codebases with LLM agents [paper] | Proposes code-as-tool paradigm through semantic code search engines |
| Synthesizer | PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents [paper] | Uses real-time personalized data to enhance the relevance of the returned results |
| Synthesizer | ChatCite: LLM agent with human workflow guidance for comparative literature summary [paper] | Mimics human methods to extract key points and write summaries for literature reviews |
| Synthesizer | PaSa: An LLM Agent for Comprehensive Academic Paper Search [paper] | Utilizes a selector to determine whether search results should be included or not |
| Simulator | Analysing utterances in llm-based user simulation for conversational search [paper] | Explores user emulators in conversation search systems for multi-round clarification |
| Simulator | Usimagent: Large language models for simulating search users [paper] | Simulates users' query, click, and stop behavior in search tasks |
| Simulator | BASES: Large-scale Web Search User Simulation with Large Language Model based Agents [paper] | Establishes a parameterized user profiling system with validation framework |
| Simulator | ChatShop: Interactive Information Seeking with Language Agents [paper] | Introduces LLM-simulated shoppers to evaluate agents' multi-turn interaction |