构建首个大规模旅行推荐数据集，支持多任务协同建模。
IntTravel: A Real-World Dataset and Generative Framework for Integrated Multi-Task Travel Recommendation
- 提出端到端生成式框架，融合出发时间、出行方式等多维信息。
- 在41亿用户交互上实现跨任务性能提升，CTR增1.09%。
- 适用于交通、导航类应用，适合做智能出行系统研发者。
下一个兴趣点（POI）推荐对现代移动服务和位置感知服务至关重要。为提供流畅用户体验，模型需综合理解旅程中的多个要素：何时出发、如何出行、去哪里以及沿途需求变化。然而，现有研究受限于碎片化数据集，仅关注下一目的地推荐，忽略出发时间、出行方式及行程中情境需求。此外，数据集规模有限，难以准确评估模型性能。为此，我们提出IntTravel，首个大规模公开的集成旅行推荐数据集，包含1.63亿用户与730万地点的41亿次交互。基于该数据集，我们设计了一个端到端的解码器专用生成框架，通过信息保留、选择与因子分解机制，在任务协作与专属性之间取得平衡，显著提升性能。该框架在IntTravel及一个非旅行基准测试中均达到领先水平。IntTravel已在高德地图上线，服务数亿用户，带来1.09%的点击率提升。数据集与代码已开源：https://github.com/AMAP-ML/IntTravel。
原文摘要 · Abstract (English)
Next Point of Interest (POI) recommendation is essential for modern mobility and location-based services. To provide a smooth user experience, models must understand several components of a journey holistically: "when to depart", "how to travel", "where to go", and "what needs arise via the route". However, current research is limited by fragmented datasets that focus merely on next POI recommendation ("where to go"), neglecting the departure time, travel mode, and situational requirements along the journey. Furthermore, the limited scale of these datasets impedes accurate evaluation of performance. To bridge this gap, we introduce IntTravel, the first large-scale public dataset for integrated travel recommendation, including 4.1 billion interactions from 163 million users with 7.3 million POIs. Built upon this dataset, we introduce an end-to-end, decoder-only generative framework for multi-task recommendation. It incorporates information preservation, selection, and factorization to balance task collaboration with specialized differentiation, yielding substantial performance gains. The framework's generalizability is highlighted by its state-of-the-art performance across both IntTravel dataset and an additional non-travel benchmark. IntTravel has been successfully deployed on Amap serving hundreds of millions of users, leading to a 1.09% increase in CTR. IntTravel is available at https://github.com/AMAP-ML/IntTravel.
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