UXSim: Towards a Hybrid User Search Simulation

2025-11-10

International Conference on Information and Knowledge Management

AI Summary

Existing approaches struggle to accurately capture the dynamic and personalized nature of user behavior in interactive search, as static agents or purely large language model (LLM)-based agents often lack grounding in real-world data. This work proposes UXSim, a novel framework that uniquely integrates traditional data-driven user simulators with adaptive LLM agents. By leveraging real user interaction logs to constrain and guide the LLM’s reasoning process, UXSim ensures behavioral fidelity while enhancing the interpretability of the underlying cognitive mechanisms. The resulting simulation not only achieves greater accuracy and dynamism in modeling user search behaviors but also enables verifiable explanations of simulated outcomes, thereby bridging the gap between data realism and generative flexibility in user modeling.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Simulating nuanced user experiences within complex interactive search systems poses distinct challenge for traditional methodologies, which often rely on static user proxies or, more recently, on standalone large language model (LLM) agents that may lack deep, verifiable grounding. The true dynamism and personalization inherent in human-computer interaction demand a more integrated approach. This work introduces UXSim. https://searchsim.org/uxsim, a novel framework that integrates both approaches. It leverages grounded data from traditional simulators to inform and constrain the reasoning of an adaptive LLM agent. This synthesis enables more accurate and dynamic simulations of user behavior while also providing a pathway for the explainable validation of the underlying cognitive processes.

Problem

• user simulation
• interactive search systems
• large language models
• user experience
• behavior modeling

Innovation

• UXSim
• hybrid simulation
• large language models
• user modeling
• explainable AI