IntTravel: A Real-World Dataset and Generative Framework for Integrated Multi-Task Travel Recommendation

TL;DR

This paper introduces IntTravel, a large-scale dataset and a generative framework for integrated multi-task travel recommendation, addressing the limitations of previous datasets by including comprehensive journey components and demonstrating improved performance and real-world deployment.

Contribution

The paper presents the first large-scale dataset for holistic travel recommendation and a novel generative model that effectively integrates multiple journey components.

Findings

IntTravel dataset contains 4.1 billion interactions from 163 million users.

The proposed framework achieves state-of-the-art results on IntTravel and other benchmarks.

Deployment on Amap increased CTR by 1.09%.

Abstract

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.