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On Generative Agents in Recommendation

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Tat-Seng ChuaAuthors Info & Claims

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1807 - 1817

https://doi.org/10.1145/3626772.3657844

Published: 11 July 2024 Publication History

Abstract

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.

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Cited By

Takata RMasumori AIkegami T(2024)Spontaneous Emergence of Agent Individuality Through Social Interactions in Large Language Model-Based CommunitiesEntropy10.3390/e2612109226:12(1092)Online publication date: 13-Dec-2024
https://doi.org/10.3390/e26121092
Chen LGao CDu XLuo HJin DLi YWang M(2024)Enhancing ID-based Recommendation with Large Language ModelsACM Transactions on Information Systems10.1145/3704263Online publication date: 13-Nov-2024
https://doi.org/10.1145/3704263
Zhang ADeng YLin YChen XWen JChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Model Powered Agents for Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661375(2989-2992)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661375
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Index Terms

On Generative Agents in Recommendation
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Conferences

SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2024

3164 pages

ISBN:9798400704314

DOI:10.1145/3626772

General Chairs:
Grace Hui Yang
Georgetown University, USA
,
Hongning Wang
Tsinghua University, China
,
Sam Han
The Washington Post, USA
,
Program Chairs:
Claudia Hauff
Spotify, Netherlands
,
Guido Zuccon
The University of Queensland, Australia
,
Yi Zhang
University of California Santa Cruz, USA

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 11 July 2024

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National Science and Technology Major Project
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SIGIR

SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 14 - 18, 2024

Washington DC, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://doi.org/10.3390/e26121092
Chen LGao CDu XLuo HJin DLi YWang M(2024)Enhancing ID-based Recommendation with Large Language ModelsACM Transactions on Information Systems10.1145/3704263Online publication date: 13-Nov-2024
https://doi.org/10.1145/3704263
Zhang ADeng YLin YChen XWen JChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Model Powered Agents for Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661375(2989-2992)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661375
Zhao YWu JWang XTang WWang Dde Rijke MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Let Me Do It For You: Towards LLM Empowered Recommendation via Tool LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657828(1796-1806)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657828
Liao JLi SYang ZWu JYuan YWang XHe XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)LLaRA: Large Language-Recommendation AssistantProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657690(1785-1795)Online publication date: 10-Jul-2024
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Portugal IAlencar PCowan D(2024)An Agentic AI-based Multi-Agent Framework for Recommender Systems2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825765(5375-5382)Online publication date: 15-Dec-2024
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