5 papers
Verifiable User Simulation for Search and Recommendation Systems
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi +3
Large-language-model (LLM) based user simulation is increasingly adopted for evaluating search engines, recommender systems, and retrieval-augmented generation pipelines, yet most…
Task-Aware Automated User Profile Generation for Recommendation Simulation Using Large Language Models
Xinye Wanyan, Chenglong Ma, Danula Hettiachchi +2
Large Language Model (LLM)-based agent simulation has emerged as a promising approach to meet the increasing demand for real-time and rigorous evaluation in modern recommender syst…
RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition
Kun Ran, Marwah Alaofi, Danula Hettiachchi +9
This paper presents the award-winning RMIT-ADM+S system for the Text-to-Text track of the NeurIPS~2025 MMU-RAG Competition. We introduce Routing-to-RAG (R2RAG), a research-focused…
Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
Xinye Wanyan, Danula Hettiachchi, Chenglong Ma +2
Large Language Models (LLMs) demonstrate human-like capabilities in language understanding, reasoning, and generation, driving interest in using LLM-based agents to simulate human…
PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Chenglong Ma, Ziqi Xu, Yongli Ren +2
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited mod…