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

• URL: http://arxiv.org/abs/2509.16895v1
• Date: Sun, 21 Sep 2025 03:10:02 GMT
• Title: Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
• Authors: Xinye Wanyan, Danula Hettiachchi, Chenglong Ma, Ziqi Xu, Jeffrey Chan,
• Abstract summary: Large Language Models (LLMs) demonstrate human-like capabilities in language understanding, reasoning, and generation.<n>Most existing approaches rely on static user profiling, neglecting the temporal and dynamic nature of user interests.<n>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.
• Score: 8.706093337738869
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• GrIT: Group Informed Transformer for Sequential Recommendation [2.631846982371029]
  Sequential recommender systems aim to predict a user's future interests by extracting temporal patterns from their behavioral history.<n>We introduce latent group representations, where each user's affiliation to these groups is modeled through learnable, time-varying membership weights.<n>A group-based representation is derived by weighting latent group embeddings with the learned membership scores.
  arXiv  Detail & Related papers  (2026-02-23T11:26:11Z)
• RecNet: Self-Evolving Preference Propagation for Agentic Recommender Systems [109.9061591263748]
  RecNet is a self-evolving preference propagation framework for recommender systems.<n>It proactively propagates real-time preference updates across related users and items.<n>In the backward phase, the feedback-driven propagation optimization mechanism simulates a multi-agent reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-01-29T12:14:31Z)
• TWICE: An LLM Agent Framework for Simulating Personalized User Tweeting Behavior with Long-term Temporal Features [22.80449819772825]
  Our framework integrates personalized user profiling, an event-driven memory module, and a workflow for personalized style rewriting.<n>Experiment results demonstrate that our framework improves personalized user simulation by effectively incorporating temporal dynamics.
  arXiv  Detail & Related papers  (2025-12-15T20:28:05Z)
• Can We Predict the Next Question? A Collaborative Filtering Approach to Modeling User Behavior [16.241726074740082]
  Large language models (LLMs) have excelled in language understanding and generation, powering advanced dialogue and recommendation systems.<n>We propose a Collaborative Filtering-enhanced Question Prediction framework to bridge the gap between language modeling and behavioral sequence modeling.
  arXiv  Detail & Related papers  (2025-11-17T04:01:20Z)
• STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning [54.28691219536054]
  We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities.<n>We develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping.<n>Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-26T08:47:58Z)
• Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors [1.8352113484137629]
  A long-standing challenge in developing accurate recommendation models is simulating user behavior, mainly due to the complex nature of user interactions.<n>We propose an approach to extracting robust user representations using a frozen Large Language Models (LLMs) and simulating cost-effective, resource-efficient user agents powered by fine-tuned Small Language Models (SLMs)<n>Our experiments provide compelling empirical evidence of the efficacy of our methods, demonstrating that user agents developed using our approach have the potential to bridge the gap between offline metrics and real-world performance of recommender systems.
  arXiv  Detail & Related papers  (2025-08-18T22:14:57Z)
• DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation [6.850757447639822]
  Large Language Models (LLMs) have gained gradual attention in recent years, by their strong semantic understanding and reasoning abilities.<n>We proposeRec (Dynamic User-Aware Language-based Recommender), which combines the temporal modelling abilities of LSTM networks with semantic reasoning power of the fine-tuned Large Language Models.
  arXiv  Detail & Related papers  (2025-07-18T14:22:05Z)
• What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context [56.590259941275434]
  RecPO is a preference optimization framework for sequential recommendation.<n>It exploits adaptive reward margins based on inferred preference hierarchies and temporal signals.<n>It mirrors key characteristics of human decision-making: favoring timely satisfaction, maintaining coherent preferences, and exercising discernment under shifting contexts.
  arXiv  Detail & Related papers  (2025-06-02T21:09:29Z)
• LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulation [94.84458417662404]
  LangTraj is a language-conditioned scene-diffusion model that simulates the joint behavior of all agents in traffic scenarios.<n>By conditioning on natural language inputs, LangTraj provides flexible and intuitive control over interactive behaviors.<n>LangTraj demonstrates strong performance in realism, language controllability, and language-conditioned safety-critical simulation.
  arXiv  Detail & Related papers  (2025-04-15T17:14:06Z)
• Enhancing User Intent for Recommendation Systems via Large Language Models [0.0]
  DUIP is a novel framework that combines LSTM networks with Large Language Models (LLMs) to dynamically capture user intent and generate personalized item recommendations.<n>Our findings suggest that DUIP is a promising approach for next-generation recommendation systems, with potential for further improvements in cross-modal recommendations and scalability.
  arXiv  Detail & Related papers  (2025-01-18T20:35:03Z)
• LIBER: Lifelong User Behavior Modeling Based on Large Language Models [42.045535303737694]
  We propose Lifelong User Behavior Modeling (LIBER) based on large language models. LIBER has been deployed on Huawei's music recommendation service and achieved substantial improvements in users' play count and play time by 3.01% and 7.69%.
  arXiv  Detail & Related papers  (2024-11-22T03:43:41Z)
• On Generative Agents in Recommendation [58.42840923200071]
  Agent4Rec is a user simulator in recommendation based on Large Language Models. Each agent interacts with personalized recommender models in a page-by-page manner.
  arXiv  Detail & Related papers  (2023-10-16T06:41:16Z)
• 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)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization [60.73540999409032]
  We show that expressive autoregressive dynamics models generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We also show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer.
  arXiv  Detail & Related papers  (2021-04-28T16:48:44Z)

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.