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

2026-02-12

AI Summary

This study addresses the limitations of existing travel recommendation research, which lacks unified modeling of multiple tasks—such as departure time, transportation mode, destination, and en-route needs—and is constrained by small-scale, fragmented data. To overcome these challenges, the authors construct IntTravel, the first large-scale multitask travel dataset encompassing 163 million users and 4.1 billion interactions, and propose a decoder-only generative multitask framework. This framework integrates information retention, task selection, and factorization mechanisms to enable collaborative modeling and differentiated balancing across the four tasks. The model achieves state-of-the-art performance on both IntTravel and general benchmarks, has been deployed in Amap (Gaode Map), and serves hundreds of millions of users, yielding a 1.09% increase in click-through rate.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• next POI recommendation
• integrated travel recommendation
• departure time
• travel mode
• situational requirements

Innovation

• multi-task travel recommendation
• generative framework
• real-world dataset
• decoder-only architecture
• integrated POI recommendation