# Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems

arXiv: https://arxiv.org/abs/2509.16895 · HTML: https://mlsanity.com/paper/2509.16895/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use.
- First posted on arXiv 21 Sep 2025.

## Placed in

- LLM agents and tool use / [Agentic recommender systems](https://mlsanity.com/agents/agentic-recommender-systems/)

## Built on by (most cited first, 7 of 7)

- [Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems](https://mlsanity.com/paper/2607.04433/) (2026)
- [Verifiable User Simulation for Search and Recommendation Systems](https://mlsanity.com/paper/2606.14474/) (2026)
- [Bridging Semantic Understanding and Popularity Bias with LLMs](https://mlsanity.com/paper/2601.09478/) (2026)
- [MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation](https://mlsanity.com/paper/2603.25126/) (2026)
- [SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation](https://mlsanity.com/paper/2607.17719/) (2026)
- [The "Curse of Knowledge" in LLM Query Simulation: Concept Provenance for Tracing Answer-Side Intrusion](https://mlsanity.com/paper/2608.25245/) (2026)
- [PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems](https://mlsanity.com/paper/2610.13989/) (2026)

## Abstract (arXiv)

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.