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.

翻译：下一兴趣点（POI）推荐是现代移动服务与基于位置服务的核心功能。为提供流畅的用户体验，模型需整体理解行程的多个组成部分："何时出发""如何出行""前往何处"以及"沿途产生何种需求"。然而，当前研究受限于仅聚焦下一POI推荐（"前往何处"）的碎片化数据集，忽视了出发时间、出行方式及行程中的情境需求。此外，这些数据集的有限规模也阻碍了性能的准确评估。为弥补这一空白，我们提出了首个面向集成旅行推荐的大规模公开数据集IntTravel，其包含来自1.63亿用户与730万个POI的41亿次交互记录。基于该数据集，我们构建了面向多任务推荐的端到端纯解码器生成式框架。该框架通过信息保持、选择与分解机制，在任务协同与专业分化间取得平衡，从而获得显著的性能提升。该框架在IntTravel数据集及额外非旅行基准测试中均取得最先进性能，充分体现了其泛化能力。IntTravel已成功部署于服务数亿用户的高德地图平台，实现点击率提升1.09%。数据集可通过https://github.com/AMAP-ML/IntTravel获取。