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DP-BPR: Destination prediction based on Bayesian personalized ranking

基于贝叶斯个性化排序的目的地预测

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Abstract

Destination prediction has attracted widespread attention because it can help vehicle-aid systems recommend related services in advance to improve user driving experience. However, the relevant research is mainly based on driving trajectory of vehicles to predict the destinations, which is challenging to achieve the early destination prediction. To this end, we propose a model of early destination prediction, DP-BPR, to predict the destinations by users’ travel time and locations. There are three challenges to accomplish the model: 1) the extremely sparse historical data make it challenge to predict destinations directly from raw historical data; 2) the destinations are related to not only departure points but also departure time so that both of them should be taken into consideration in prediction; 3) how to learn destination preferences from historical data. To deal with these challenges, we map sparse high-dimensional data to a dense low-dimensional space through embedding learning using deep neural networks. We learn the embeddings not only for users but also for locations and time under the supervision of historical data, and then use Bayesian personalized ranking (BPR) to learn to rank destinations. Experimental results on the Zebra dataset show the effectiveness of DP-BPR.

摘要

目的地预测可以帮助车辆辅助系统提前推荐相关服务, 改善用户的驾驶体验, 受到研究者的广泛关注. 然而, 相关研究主要是基于车辆的行驶轨迹来预测目的地, 难以实现早期的目的地预测. 为此, 本论文提出了一个早期目的地预测模型 DP-BPR, 通过用户的出行时间和地点来预测目的地. 该模型的实现有三个方面的挑战: 1) 稀疏的历史数据使得直接从原始数据中预测目的地非常困难; 2) 目的地不仅与出发点有关, 而且与出发时间有关, 在预测时应将两者都考虑在内; 3) 如何从历史数据中准确地学习目的地偏好. 为了应对这些挑战, 我们利用深度神经网络将稀疏的高维数据映射到稠密的低维空间, 并学习用户、 位置和时间的嵌入, 然后, 使用贝叶斯个性化排序学习并对目的地进行排名. 在 Zebra 数据集上进行了实验, 实验结果表明了 DP-BPR 的有效性.

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Authors and Affiliations

  1. School of Finance and Management, Chongqing Business Vocational College, Chongqing, 401331, China

    Feng Jiang  (江峰)

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, 400044, China

    Feng Jiang  (江峰), Zhen-ni Lu  (卢珍妮), Min Gao  (高旻) & Da-ming Luo  (罗大明)

  3. School of Big Data and Software Engineering, Chongqing University, Chongqing, 400044, China

    Zhen-ni Lu  (卢珍妮) & Min Gao  (高旻)

  4. Chongqing Jiaxun Construction Engineering Co., Ltd., Chongqing, 401147, China

    Da-ming Luo  (罗大明)

Authors
  1. Feng Jiang  (江峰)
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  2. Zhen-ni Lu  (卢珍妮)
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  3. Min Gao  (高旻)
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  4. Da-ming Luo  (罗大明)
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Contributions

JIANG Feng developed the overarching research goals. JIANG Feng and LU Zhen-ni wrote the initial draft of the manuscript. LU Zhen-ni and GAO Min carried out the experiments. JIANG Feng and LUO Da-ming analyzed the experimental results.

Corresponding author

Correspondence to Feng Jiang  (江峰).

Ethics declarations

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Foundation item: Project (2018YFF0214706) supported by the National Key Research and Development Program of China; Project (cstc2020jcyj-msxmX0690) supported by the Natural Science Foundation of Chongqing, China; Project (2020CDJ-LHZZ-039) supported by the Fundamental Research Funds for the Central Universities of Chongqing, China; Project(cstc2019jscx-fxydX0012) supported by the Key Research Program of Chongqing Technology Innovation and Application Development, China

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Jiang, F., Lu, Zn., Gao, M. et al. DP-BPR: Destination prediction based on Bayesian personalized ranking. J. Cent. South Univ. 28, 494–506 (2021). https://doi.org/10.1007/s11771-021-4617-x

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  • DOI: https://doi.org/10.1007/s11771-021-4617-x

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