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On Generative Agents in Recommendation

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arxiv 2310.10108 v3 pith:3D5GD7QV submitted 2023-10-16 cs.IR cs.AI

An Zhang , Yuxin Chen , Leheng Sheng , Xiang Wang , Tat-Seng Chua This is my paper · ORCID
classification cs.IRcs.AI
keywords agentsrecommendationagent4recmodulesrecommendergenerativeactionsemotion-driven
verification ladder T0 review T1 audit T2 compute T3 formal
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Recommender systems are the cornerstone of today's information dissemination, yet a disconnect between offline metrics and online performance greatly hinders their development. Addressing this challenge, we envision a recommendation simulator, capitalizing on recent breakthroughs in human-level intelligence exhibited by Large Language Models (LLMs). We propose Agent4Rec, a user simulator in recommendation, leveraging LLM-empowered generative agents equipped with user profile, memory, and actions modules specifically tailored for the recommender system. In particular, these agents' profile modules are initialized using real-world datasets (e.g. MovieLens, Steam, Amazon-Book), capturing users' unique tastes and social traits; memory modules log both factual and emotional memories and are integrated with an emotion-driven reflection mechanism; action modules support a wide variety of behaviors, spanning both taste-driven and emotion-driven actions. Each agent interacts with personalized recommender models in a page-by-page manner, relying on a pre-implemented collaborative filtering-based recommendation algorithm. We delve into both the capabilities and limitations of Agent4Rec, aiming to explore an essential research question: ``To what extent can LLM-empowered generative agents faithfully simulate the behavior of real, autonomous humans in recommender systems?'' Extensive and multi-faceted evaluations of Agent4Rec highlight both the alignment and deviation between agents and user-personalized preferences. Beyond mere performance comparison, we explore insightful experiments, such as emulating the filter bubble effect and discovering the underlying causal relationships in recommendation tasks. Our codes are available at https://github.com/LehengTHU/Agent4Rec.

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Fixed Depths and Widths: Optimizing Textual Decoding Tries in LLM-based Generative Recommendation

    cs.IR 2026-07 conditional novelty 6.0 of 10

    BONSAI constructs variable-depth, low-branching decoding tries for LLM-based generative recommendation and reports 16–22% relative gains over state-of-the-art baselines.

  2. Co-Saving: Resource Aware Multi-Agent Collaboration for Software Development

    cs.CL 2025-05 reject novelty 6.0 of 10

    Co-Saving cuts token usage by roughly half in multi-agent software development by injecting learned shortcut instructions that bypass intermediate reasoning steps, while slightly improving a composite code-quality score.

  3. Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration

    cs.CL 2025-05 conditional novelty 5.0 of 10

    MAEL gives each agent in a multi-agent LLM system an experience pool and retrieves high-reward past steps to guide new task solving.

  4. Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models

    cs.IR 2024-12 conditional novelty 5.0 of 10

    RSLLM mixes item ID embeddings from classical recommenders with text titles inside an LLM prompt and uses two-stage contrastive fine-tuning to improve sequential recommendation.

  5. Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems

    cs.CY 2025-01 conditional novelty 4.0 of 10

    Agent4Edu generates learner responses using LLM agents with profile, memory, and action modules, and shows small improvements over prior simulators and in CAT model training.

  6. The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit

    cs.IR 2025-01 reject novelty 4.0 of 10

    A GCN retriever plus multi-head early exit speeds up LLM click-through rate prediction, but the reported AUC numbers are internally inconsistent.

  7. Improving GenIR Systems Based on User Feedback

    cs.IR 2025-01 conditional novelty 2.0 of 10

    A survey of user feedback techniques for improving generative information retrieval systems, covering alignment, continual learning, conversational learning, and prompt learning.

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