IntTravel: A Real-World Dataset and Generative Framework for Integrated Multi-Task Travel Recommendation
AMAP, Alibaba Group
¹Equal contribution †Corresponding author and project lead.
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. IntTravel has been successfully deployed on Amap serving hundreds of millions of users.
All data are collected from a leading provider of digital map, navigation and real-time traffic information in China. Here is a simple dataset in data_process/raw_data and a more comprehensive dataset in Hugging Face. The code in data_process demonstrates how to construct the input sequence of the model and the labels for all tasks based on the original data.
The IntTravel dataset contains 7,291,872 POIs (Point of Interests) distributed across several major cities in China. Each POI is described by the following fields:
| Field | Description |
|---|---|
| POI ID | A unique identifier for each Point of Interest. |
| Normalized score | A 0-1 score reflecting the overall popularity of the POI. |
| Geographic ID | Identifier for the POI's geographic block. Same GIDs indicate geographical proximity. |
| Category ID | A numerical identifier for the Point of Interest's category. |
| Administrative Region ID | The identifier for the administrative region of the POI. |
| Coordinates | The spatial coordinates of the POI on a 2D plane. |
The IntTravel dataset contains 162,815,861 users, each described by the following fields:
| Field | Description |
|---|---|
| User ID | A unique identifier assigned to each user. |
| Profile Feature 1 | The first profile feature. |
| ... | ... |
| Profile Feature 6 | The sixth profile feature. |
The IntTravel dataset includes 4,129,827,011 user interaction events. Each event is characterized by the following fields:
| Field | Description |
|---|---|
| User ID | A unique identifier for the user who performed the interaction. |
| Timestamp | The time of the user interaction, recorded in milliseconds. |
| Action Type | A numerical ID representing the type of user behavior (e.g., click). |
| POI ID | The identifier of the Point of Interest involved in the interaction. |
| Geographic ID | The geographic block ID where the user was during the interaction. |
| Administrative Region ID | The administrative region ID where the user was during the interaction. |
| Weather | A numerical ID representing the weather condition during the interaction. |
| Travel Mode | A numerical ID for the user's chosen travel mode. |
| Via POI ID | The identifier for a way-point POI added by the user. |
IntTravel is the first multi-task solution for generative recommendation. We propose a bottom-up multi-task method to handle multiple tasks within a single generative model. The approach comprises three modules:
- Task-Guided Information Persistence (TIP) ensures maximum propagation of task-relevant information in the decoder.
- Task-Specific Selective Gating (TSG) enables each task to filter useful information from the decoder's output.
- Task-Aware Scenario Factorization (TSF) empowers each task to factorize its output based on specific scenarios.
The results of scaling experiments reveal a strong and consistent scaling trend. As the model depth increases from 1 to 80 layers, performance on all metrics steadily improves (task accuracy increases outward while loss decreases outward). There is no sign of performance decrease even at very deep configurations like 80 layers, which is often a challenge for complex models. This demonstrates the excellent scaling capability of proposed architecture.