用动态时间感知模型提升推荐系统中用户行为模拟真实度
Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
- 构建动态更新的用户画像，实时反映兴趣变化
- 通过时间增强提示，捕捉用户行为序列模式
- 适合需要高仿真用户数据的推荐系统研究与开发
大型语言模型（LLMs）在语言理解、推理和生成方面表现出类人能力，推动了利用基于LLM的智能体模拟推荐系统中人类反馈的研究。然而，现有方法多依赖静态用户画像，忽略了用户兴趣随时间演变的动态特性。这一局限源于语言建模与行为建模之间的脱节，限制了智能体对序列模式的表征能力。为此，我们提出一种面向推荐系统的动态时间感知代理模拟器DyTA4Rec，使智能体能够基于历史交互建模并利用不断演化的用户行为。DyTA4Rec包含动态更新机制以实现实时画像优化、时间增强提示以捕捉序列上下文，并采用自适应聚合策略生成连贯反馈。在群体与个体层面的实验结果表明，通过建模动态特征并增强时间感知能力，DyTA4Rec显著提升了模拟行为与实际用户行为的一致性。
原文摘要 · Abstract (English)
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.
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