Verifiable User Simulation for Search and Recommendation Systems

• URL: http://arxiv.org/abs/2606.14474v1
• Date: Fri, 12 Jun 2026 14:09:56 GMT
• Title: Verifiable User Simulation for Search and Recommendation Systems
• Abstract summary: Large-language-model (LLM) based user simulation is increasingly adopted for evaluating search engines, recommender systems, and retrieval-augmented generation pipelines.<n>This tutorial introduces a proposed design-and-audit framework that treats a user simulator as a verifiable engineering artefact composed of seven auditable components.
• Score: 9.794615910946002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-language-model (LLM) based user simulation is increasingly adopted for evaluating search engines, recommender systems, and retrieval-augmented generation pipelines, yet most simulators remain opaque: it is difficult to determine why a simulated user made a particular choice or whether that choice is consistent with the intended user profile. Compounding this, recent research shows that LLMs can produce biased or discriminatory responses depending on user background characteristics such as language, education level, and cultural context, raising concerns about the equitable treatment of minority and disadvantaged groups. This half-day, in-person tutorial introduces a proposed design-and-audit framework that treats a user simulator as a verifiable engineering artefact composed of seven auditable components - structured Persona, task-aware Contract, matched human-vs-agent Execution, auditable Trace, persona-aligned Verification, structured Feedback, and a Refinement loop that updates personas and contracts. Through two hands-on mini-labs on recommendation-list evaluation and search-query formulation, participants will inspect simulator behaviour end-to-end, distinguish diagnostic discrepancy analysis from statistical validation, and apply checks for fidelity, credibility, and demographic bias. The tutorial targets information retrieval and recommender systems researchers and practitioners interested in user behaviour simulation and responsible AI.

Related papers

• Investigating Assistant Bias in LLM User Simulators Using a Role Vector [21.70379807235766]
  LLM-based user simulators are increasingly used to evaluate autonomous agents at scale.<n>Assistant bias is baked in during model training, which role-playing prompts fail to override.<n>We observe two findings: (i) the user direction is identifiable in activations, elicits user-like behaviors, and captures characteristics distinct from assistant traits.
  arXiv  Detail & Related papers  (2026-09-01T02:49:30Z)
• Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors [61.610957638373826]
  We introduce a method to measure the distributional gap between real and simulated user behaviors.<n>Given a dataset of real and simulated conversations, our method extracts representations of user behavior from each conversation.<n>We show that most simulators behave similarly, while a few stand apart.
  arXiv  Detail & Related papers  (2026-05-08T15:09:25Z)
• Mind the Sim2Real Gap in User Simulation for Agentic Tasks [101.69142591891234]
  We formalize the Sim2Real gap in user simulation and present the first study running the full $$-bench protocol with real humans.<n> Behaviorally, LLM simulators are excessively cooperative, stylistically uniform, and lack realistic frustration or ambiguity.<n>These findings highlight the importance of human validation when using LLM-based user simulators in the agent development cycle.
  arXiv  Detail & Related papers  (2026-03-11T19:12:31Z)
• RecThinker: An Agentic Framework for Tool-Augmented Reasoning in Recommendation [63.74915464611075]
  RecThinker is an agentic framework for tool-augmented reasoning in recommendation.<n>We develop a suite of specialized tools for RecThinker, enabling the model to acquire user item-side and collaborative information.
  arXiv  Detail & Related papers  (2026-03-10T16:07:17Z)
• ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders [48.83868690303791]
  We introduce ConvApparel, a new dataset of human-AI conversations designed to address this gap.<n>Its unique dual-agent data collection protocol -- using both "good" and "bad" recommenders -- enables counterfactual validation.<n>We propose a comprehensive validation framework that combines statistical alignment, a human-likeness score, and counterfactual validation.
  arXiv  Detail & Related papers  (2026-02-18T23:00:21Z)
• 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)
• LLM-Powered User Simulator for Recommender System [29.328839982869923]
  We introduce an LLM-powered user simulator to simulate user engagement with items in an explicit manner.<n>Specifically, we identify the explicit logic of user preferences, leverage LLMs to analyze item characteristics and distill user sentiments.<n>We propose an ensemble model that synergizes logical and statistical insights for user interaction simulations.
  arXiv  Detail & Related papers  (2024-12-22T12:00:04Z)
• A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems [14.646529557978512]
  Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences. Large Language Models (LLMs) has marked the onset of a new epoch in computational capabilities. We introduce a Controllable, scalable, and human-Involved (CSHI) simulator framework that manages the behavior of user simulators.
  arXiv  Detail & Related papers  (2024-05-13T03:02:56Z)
• USimAgent: Large Language Models for Simulating Search Users [33.17004578463697]
  We introduce a Large Language Models-based user search behavior simulator, USimAgent. The simulator can simulate users' querying, clicking, and stopping behaviors during search. Empirical investigation on a real user behavior dataset shows that the simulator outperforms existing methods in query generation.
  arXiv  Detail & Related papers  (2024-03-14T07:40:54Z)
• BASES: Large-scale Web Search User Simulation with Large Language Model based Agents [108.97507653131917]
  BASES is a novel user simulation framework with large language models (LLMs) Our simulation framework can generate unique user profiles at scale, which subsequently leads to diverse search behaviors. WARRIORS is a new large-scale dataset encompassing web search user behaviors, including both Chinese and English versions.
  arXiv  Detail & Related papers  (2024-02-27T13:44:09Z)
• 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)
• Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems [80.77917437785773]
  Task-oriented dialogue systems ( TDSs) are assessed mainly in an offline setting or through human evaluation. We propose a metaphorical user simulator for end-to-end TDS evaluation, where we define a simulator to be metaphorical if it simulates user's analogical thinking in interactions with systems. We also propose a tester-based evaluation framework to generate variants, i.e., dialogue systems with different capabilities.
  arXiv  Detail & Related papers  (2022-04-02T05:11:03Z)

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.