Abstract
Recommending accurately pick-up area with sparse GPS data is valuable and still challenging to increase taxi drivers’ profits and reduce fuel consumption. In recent years, the recommendation approach based on matrix factorization has been proposed to deal with sparsity. However, it is not accurate enough due to the regardless of the interaction effect between features. Therefore, this paper proposes DeepFM-based taxi pick-up area recommendation. Firstly, the research area is divided into grid area of equal size, the pick-up point information is extracted from the original GPS trajectory data, the pick-up point information and POI data are mapped to the grid area, the corresponding grid attributes are calculated and the grid feature matrix is constructed; Then, DeepFM is used to mine the combined relationship between the grid features, combining spatial information to recommend the most suitable grid area for drivers; Finally, the performance evaluation is carried out using DiDi's public data. The experimental results show that this method can significantly improve the quality of the recommended results and is superior to some existing recommended methods.
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Acknowledgments
This project was funded by the National Natural Science Foundation of China (41871320), Provincial and Municipal Joint Fund of Hunan Provincial Natural Science Foundation of China (2018JJ4052), Hunan Provincial Natural Science Foundation of China (2017JJ2081), the Key Project of Hunan Provincial Education Department (17A070, 19A172, 19A174), the Scientific Research Project of Hunan Education Department (19C0755).
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Wang, X., Liu, Y., Liao, Z., Zhao, Y. (2021). DeepFM-Based Taxi Pick-Up Area Recommendation. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_36
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DOI: https://doi.org/10.1007/978-3-030-68821-9_36
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