Deep Attentional Factorization Machines Learning Approach for Driving Safety Risk Prediction

doi:10.1088/1742-6596/1732/1/012007

	Deep Attentional Factorization Machines Learning Approach for Driving Safety Risk Prediction
	Zhang,Jun 1,2,3; Wu,Zhongcheng 2,3; Li,Fang 2; Li,Wenjing 2,3; Ren,Tingting 2; Li,Wei 2; Chen,Jie 2,3
刊名	Journal of Physics: Conference Series
	2021
卷号	1732
ISSN号	1742-6588
DOI	10.1088/1742-6596/1732/1/012007
英文摘要	Abstract The data of Internet of Vehicles (IoV) can be used to evaluate the driving safety risk of auto insurance policyholder and provide technical means for Usage Based Insurance (UBI). There are many types of IoV data, such as continuous or ordinal, categorical, binary etc., which contain highly sparse and dimensional features after One-Hot processing, thus they learning the interaction between critical features and training predictive model difficult. Furthermore, some of the available data have been desensitized, so it is impossible to perform feature engineering based on experience. We propose an end-to-end deep learning framework named Deep Attentional Factorization Machine (DeepAFM), which combines the power of attentional factorization machine with deep learning for feature learning in a new neural network architecture. Compared with existing deep learning models, our approach can learn the weighted interactions between various features effectively by introducing the structure of feature fields without feature engineering. Experimental results showed that our model yields excellent results in real-world data.
语种	英语
出版者	IOP Publishing
WOS记录号	IOP:JPCS_1732_1_012007
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/127367]
专题	中国科学院合肥物质科学研究院
作者单位	1.Institute of Intelligent Machines, and Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China 2.High Magnetic Field Laboratory, and Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China 3.University of Science and Technology of China, Hefei 230026, China
推荐引用方式 GB/T 7714	Zhang,Jun,Wu,Zhongcheng,Li,Fang,et al. Deep Attentional Factorization Machines Learning Approach for Driving Safety Risk Prediction[J]. Journal of Physics: Conference Series,2021,1732.
APA	Zhang,Jun.,Wu,Zhongcheng.,Li,Fang.,Li,Wenjing.,Ren,Tingting.,...&Chen,Jie.(2021).Deep Attentional Factorization Machines Learning Approach for Driving Safety Risk Prediction.Journal of Physics: Conference Series,1732.
MLA	Zhang,Jun,et al."Deep Attentional Factorization Machines Learning Approach for Driving Safety Risk Prediction".Journal of Physics: Conference Series 1732(2021).