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The authors introduce IntTravel, a large-scale dataset with 4.1 billion interactions for integrated travel recommendation, addressing the limitations of existing datasets that focus solely on next POI recommendation. To leverage this dataset, they propose a decoder-only generative framework that balances task collaboration and differentiation through information preservation, selection, and factorization. Experiments demonstrate state-of-the-art performance on IntTravel and another benchmark dataset, with a successful deployment on Amap resulting in a 1.09% CTR increase.
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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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