Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems

• URL: http://arxiv.org/abs/2505.16429v1
• Date: Thu, 22 May 2025 09:14:23 GMT
• Title: Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
• Authors: Song Jin, Juntian Zhang, Yuhan Liu, Xun Zhang, Yufei Zhang, Guojun Yin, Fei Jiang, Wei Lin, Rui Yan,
• Abstract summary: RecInter is a novel agent-based simulation platform for recommender systems.<n>In RecInter, simulated user actions (e.g., likes, reviews, purchases) dynamically update item attributes in real-time.<n> Merchant Agents can reply, fostering a more realistic and evolving ecosystem.
• Score: 40.09105175322562
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Evaluating and iterating upon recommender systems is crucial, yet traditional A/B testing is resource-intensive, and offline methods struggle with dynamic user-platform interactions. While agent-based simulation is promising, existing platforms often lack a mechanism for user actions to dynamically reshape the environment. To bridge this gap, we introduce RecInter, a novel agent-based simulation platform for recommender systems featuring a robust interaction mechanism. In RecInter platform, simulated user actions (e.g., likes, reviews, purchases) dynamically update item attributes in real-time, and introduced Merchant Agents can reply, fostering a more realistic and evolving ecosystem. High-fidelity simulation is ensured through Multidimensional User Profiling module, Advanced Agent Architecture, and LLM fine-tuned on Chain-of-Thought (CoT) enriched interaction data. Our platform achieves significantly improved simulation credibility and successfully replicates emergent phenomena like Brand Loyalty and the Matthew Effect. Experiments demonstrate that this interaction mechanism is pivotal for simulating realistic system evolution, establishing our platform as a credible testbed for recommender systems research.

Related papers

• PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation [9.841963696576546]
  Personality-driven User Behaviour Simulator (PUB) integrates the Big Five personality traits to model personalised user behaviour.<n>PUB dynamically infers user personality from behavioural logs (e.g., ratings, reviews) and item metadata, then generates synthetic interactions that preserve statistical fidelity to real-world data.<n> Experiments on the Amazon review datasets show that logs generated by PUB closely align with real user behaviour and reveal meaningful associations between personality traits and recommendation outcomes.
  arXiv  Detail & Related papers  (2025-06-05T01:57:36Z)
• GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulation [2.7083394633019973]
  We propose a high-fidelity social simulation platform to realistically simulate user behavior evolution under recommendation interventions.<n>The system comprises a population of Sim-User Agents equipped with a five-layer cognitive architecture that encapsulates key psychological mechanisms.<n>In particular, we introduce the Intimacy--Curiosity--Reciprocity--Risk (ICR2) motivational engine grounded in psychological and sociological theories.
  arXiv  Detail & Related papers  (2025-05-27T13:09:21Z)
• AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations [12.492232195149661]
  We present AgentDynEx, an AI system that helps set up simulations from user-specified mechanics and dynamics.<n>A technical evaluation found that nudging enables simulations to have more complex mechanics and maintain its notable dynamics compared to simulations without nudging.
  arXiv  Detail & Related papers  (2025-04-13T17:26:35Z)
• CreAgent: Towards Long-Term Evaluation of Recommender System under Platform-Creator Information Asymmetry [55.64992650205645]
  We propose CreAgent, a large language model-empowered creator simulation agent.<n>By incorporating game theory's belief mechanism and the fast-and-slow thinking framework, CreAgent effectively simulates creator behavior.<n>Our credibility validation experiments show that CreAgent aligns well with the behaviors between real-world platform and creator.
  arXiv  Detail & Related papers  (2025-02-11T07:09:49Z)
• A Simulation System Towards Solving Societal-Scale Manipulation [14.799498804818333]
  The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world settings at scale is ethically and logistically impractical. We present a simulation environment designed to address this.
  arXiv  Detail & Related papers  (2024-10-17T03:16:24Z)
• Very Large-Scale Multi-Agent Simulation in AgentScope [112.98986800070581]
  We develop new features and components for AgentScope, a user-friendly multi-agent platform. We propose an actor-based distributed mechanism towards great scalability and high efficiency. We also provide a web-based interface for conveniently monitoring and managing a large number of agents.
  arXiv  Detail & Related papers  (2024-07-25T05:50:46Z)
• On Generative Agents in Recommendation [58.42840923200071]
  Agent4Rec is a user simulator in recommendation based on Large Language Models. Each agent interacts with personalized recommender models in a page-by-page manner.
  arXiv  Detail & Related papers  (2023-10-16T06:41:16Z)
• INTAGS: Interactive Agent-Guided Simulation [4.04638613278729]
  In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production. We propose a metric to distinguish between real and synthetic multi-agent systems, which is evaluated through the live interaction between the Exp and BG agents. We show that using INTAGS to calibrate the simulator can generate more realistic market data compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network approach.
  arXiv  Detail & Related papers  (2023-09-04T19:56:18Z)
• User Behavior Simulation with Large Language Model based Agents [116.74368915420065]
  We propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans.
  arXiv  Detail & Related papers  (2023-06-05T02:58:35Z)
• Realistic simulation of users for IT systems in cyber ranges [63.20765930558542]
  We instrument each machine by means of an external agent to generate user activity. This agent combines both deterministic and deep learning based methods to adapt to different environment. We also propose conditional text generation models to facilitate the creation of conversations and documents.
  arXiv  Detail & Related papers  (2021-11-23T10:53:29Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning [1.5293427903448025]
  This paper presents a framework to mimic and predict user, specifically driver, behaviors in ride-hailing services. We use a data-driven hybrid reinforcement learning and imitation learning approach for this. Our framework provides an ideal playground for ride-hailing platforms to experiment with platform-specific parameters to predict drivers' behavioral patterns.
  arXiv  Detail & Related papers  (2021-04-06T16:49:26Z)

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.