用动态世界知识增强推荐，让模型懂用户去哪的真正原因。
Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation
- 用LLM代理生成时空感知的本地文化叙事
- 在三个数据集上相对提升最高达12.4%
- 适合做个性化文旅、出行推荐的研究者
基于大语言模型的生成式地点推荐模型虽已取得良好效果，但其内部知识在训练后固定不变，难以捕捉影响用户出行决策的实时现实因素，如本地活动和文化趋势。为此，我们提出AWARE（Agent-based World knowledge Augmented REcommendation），通过一个LLM代理生成与位置和时间相关的上下文叙事，捕捉区域文化特征、季节性趋势及正在进行的事件。该方法并非引入通用或噪声信息，而是将外部世界知识锚定在用户的个人行为上下文中，使其与用户的时空行为模式相融合。在三个真实世界数据集上的大量实验表明，AWARE始终优于现有基线模型，相对性能提升最高达12.4%。
原文摘要 · Abstract (English)
Generative point-of-interest (POI) recommendation models based on large language models (LLMs) have shown promising results by formulating next POI prediction as a sequence generation task. However, the knowledge encoded in these models remains fixed after training, making them unable to perceive evolving real-world conditions that shape user mobility decisions, such as local events and cultural trends. To bridge this gap, we propose AWARE (Agent-based World knowledge Augmented REcommendation), which employs an LLM agent to generate location- and time-aware contextual narratives that capture regional cultural characteristics, seasonal trends, and ongoing events relevant to each user. Rather than introducing generic or noisy information, AWARE further anchors these narratives in each user's behavioral context, grounding external world knowledge in personalized spatial-temporal patterns. Extensive experiments on three real-world datasets demonstrate that AWARE consistently outperforms competitive baselines, achieving up to 12.4% relative improvement.
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