Abstract
The increasingly built intercity transportation enables people to visit surrounding cities conveniently. Hence it is becoming a hot research topic to predict where a traveler would visit in a surrounding city based on check-in data collected from location-based mobile Apps. However, as most users rarely travel out of hometown, there is a high skew of the quantity of check-in data between hometown and surrounding cities. Suffering from the severe sparsity of user mobility data in surrounding city, existing approaches do not perform well as they can hardly maintain travelers’ intrinsic preference and meanwhile adapt to travelers’ interest drift. To address these concerns, in this paper, taking cross-city travelers as the medium, we propose a novel framework called CityTrans to transfer traveler mobility knowledge from hometown city to surrounding city, which considers both the long-term preference in hometown city and short-term interest drift in surrounding city. Various attention mechanisms are leveraged to obtain traveler representation enriched by long-term and short-term preferences. Besides, we propose to portray POIs through GNN incorporating POI attributes and geographical information. Finally, the traveler and POI representations are combined for prediction. To train the framework, the transfer loss as well as the prediction loss are jointly optimized. Extensive experiments on real-world datasets validate the superiority of our framework over several state-of-the-art approaches.
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Notes
- 1.
For clarity, in this paper, we refer to people’s residential city or working city as the hometown city, and refer to others as the out-of-town city. Besides, if an out-of-town city is close to the hometown city, we refer to it as the surrounding city.
- 2.
- 3.
Please note that we still regard those travelers with only one check-in record in the surrounding city as non-cross-city travelers, because their only check-in record in the surrounding city needs to be used for cold-start prediction.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20210280, in part by the Fundamental Research Funds for the Central Universities under Grant NS2022089, and in part by the Jiangsu Provincial Innovation and Entrepreneurship Doctor Program under Grant JSSCBS20210185.
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Xu, S., Xu, J., Li, B., Fu, X. (2023). Predicting Where You Visit in a Surrounding City: A Mobility Knowledge Transfer Framework Based on Cross-City Travelers. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13943. Springer, Cham. https://doi.org/10.1007/978-3-031-30637-2_22
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