Large language Model’s (LLMs) have shown high-quality semantic comprehension ability and extensive external knowledge, which has been incorporated into recommendation systems as multiple functions. However, existing bias evaluation pipelines designed for conventional recommendation systems are not fully applicable to recommendation systems via LLM (RecLLM) and most bias mitigation methods are limited to a single intervention stage, rendering them inadequate for addressing the overall bias of the complex RecLLMs. Xinye will introduce a comprehensive evaluation framework designed to assess the biases within RecLLMs and their constituent sub-modules (Wanyan et al., 2025). In addition, a calibrated synthetic benchmark dataset, leveraging LLMs, will be developed to facilitate the bias evaluation and mitigation experiments.
Xinye is a scholarship recipient of the ARC Centre for Automated Decision-Making and Society (ADM+S) is supervised by Prof. Jeffrey Chan and Dr. Danula Hettiachchi.
References
CIKM
Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
Large Language Models (LLMs) demonstrate human-like capabilities in language understanding, reasoning, and generation, driving interest in using LLM-based agents to simulate human feedback in recommender systems. However, most existing approaches rely on static user profiling, neglecting the temporal and dynamic nature of user interests. This limitation stems from a disconnect between language modelling and behaviour modelling, which constrains the capacity of agents to represent sequential patterns. To address this challenge, we propose a Dynamic Temporal-aware Agent-based simulator for Recommender Systems, DyTA4Rec, which enables agents to model and utilise evolving user behaviour based on historical interactions. DyTA4Rec features a dynamic updater for real-time profile refinement, temporal-enhanced prompting for sequential context, and self-adaptive aggregation for coherent feedback. Experimental results at group and individual levels show that DyTA4Rec significantly improves the alignment between simulated and actual user behaviour by modelling dynamic characteristics and enhancing temporal awareness in LLM-based agents.
@inproceedings{Wanyan2025-ta,title={Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems},author={Wanyan, Xinye and Hettiachchi, Danula and Ma, Chenglong and Xu, Ziqi and Chan, Jeffrey},booktitle={Proceedings of the 34th ACM International Conference on Information and Knowledge Management},series={CIKM '25},year={2025},publisher={ACM},note={To appear},}
