Papers User Simulation
“User Simulation” 태그가 달린 논문 64편 · 필터 해제
ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues?
In this paper, we introduce ECom-Bench, the first benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain. ECom-Bench features dynamic user simulation based on …
User SimulationThe Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
Large language models (LLMs) are rapidly evolving from passive engines of text generation into agentic entities that can plan, remember, invoke external tools, and co-operate with one another. This perspective paper inve…
Explanation GenerationHallucinationRecommendation SystemsText Generation+1Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent
Interactive recommendation is a typical information-seeking task that allows users to interactively express their needs through natural language and obtain personalized recommendations. Large language model-powered (LLM-…
Interactive RecommendationLarge Language ModelRecommendation SystemsUser SimulationLLM-Based User Simulation for Low-Knowledge Shilling Attacks on Recommender Systems
Recommender systems (RS) are increasingly vulnerable to shilling attacks, where adversaries inject fake user profiles to manipulate system outputs. Traditional attack strategies often rely on simplistic heuristics, requi…
Language ModelingLanguage ModellingLarge Language ModelRecommendation Systems+2Exploring Human-Like Thinking in Search Simulations with Large Language Models
Simulating user search behavior is a critical task in information retrieval, which can be employed for user behavior modeling, data augmentation, and system evaluation. Recent advancements in large language models (LLMs)…
Data AugmentationInformation RetrievalUser SimulationExploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Conversational Recommender Systems (CRSs) engage users in multi-turn interactions to deliver personalized recommendations. The emergence of large language models (LLMs) further enhances these systems by enabling more nat…
Conversational RecommendationRecommendation SystemsUser SimulationUser Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation
User simulation is an emerging interdisciplinary topic with multiple critical applications in the era of Generative AI. It involves creating an intelligent agent that mimics the actions of a human user interacting with a…
Synthetic Data GenerationUser SimulationVicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity
Scenario-based training has been widely adopted in many public service sectors. Recent advancements in Large Language Models (LLMs) have shown promise in simulating diverse personas to create these training scenarios. Ho…
User SimulationSimulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues
Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialog…
Hierarchical Reinforcement LearningReinforcement Learning (RL)User SimulationLMAgent: A Large-scale Multimodal Agents Society for Multi-user Simulation
The believable simulation of multi-user behavior is crucial for understanding complex social systems. Recently, large language models (LLMs)-based AI agents have made significant progress, enabling them to achieve human-…
User SimulationStop Playing the Guessing Game! Target-free User Simulation for Evaluating Conversational Recommender Systems
Recent approaches in Conversational Recommender Systems (CRSs) have tried to simulate real-world users engaging in conversations with CRSs to create more realistic testing environments that reflect the complexity of huma…
Recommendation SystemsUser SimulationVideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation
Large multimodal models (LMMs) with advanced video analysis capabilities have recently garnered significant attention. However, most evaluations rely on traditional methods like multiple-choice questions in benchmarks su…
ChatbotMultiple-choiceUser SimulationVideo UnderstandingAgentic Feedback Loop Modeling Improves Recommendation and User Simulation
Large language model-based agents are increasingly applied in the recommendation field due to their extensive knowledge and strong planning capabilities. While prior research has primarily focused on enhancing either the…
Large Language ModelUser SimulationMulti-trait User Simulation with Adaptive Decoding for Conversational Task Assistants
Conversational systems must be robust to user interactions that naturally exhibit diverse conversational traits. Capturing and simulating these diverse traits coherently and efficiently presents a complex challenge. This…
DiversityUser SimulationMinimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold standard'' comes at a high cost, however,…
Recommendation SystemsUser SimulationReformulating Conversational Recommender Systems as Tri-Phase Offline Policy Learning
Existing Conversational Recommender Systems (CRS) predominantly utilize user simulators for training and evaluating recommendation policies. These simulators often oversimplify the complexity of user interactions by focu…
Recommendation SystemsUser SimulationTowards a Formal Characterization of User Simulation Objectives in Conversational Information Access
User simulation is a promising approach for automatically training and evaluating conversational information access agents, enabling the generation of synthetic dialogues and facilitating reproducible experiments at scal…
Conversational Information AccessUser SimulationIdentifying Breakdowns in Conversational Recommender Systems using User Simulation
We present a methodology to systematically test conversational recommender systems with regards to conversational breakdowns. It involves examining conversations generated between the system and simulated users for a set…
Conversational RecommendationDiagnosticUser SimulationLusifer: LLM-based User SImulated Feedback Environment for online Recommender systems
Reinforcement learning (RL) recommender systems often rely on static datasets that fail to capture the fluid, ever changing nature of user preferences in real-world scenarios. Meanwhile, generative AI techniques have eme…
Collaborative FilteringRecommendation Systemsreinforcement-learningReinforcement Learning+2Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study
Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community for its ability to leverage massive logged user interaction data to train ranking models. While the CLTR models can be theoreti…
counterfactualLearning-To-RankUser Simulation