摘要
车货匹配平台中存有大量的历史车货数据,通过这些历史数据可以分析获得司机对不同货物的兴趣度,预测司机所选取货物的种类(司机点击率预测),从而为司机推荐合适的货物,实现精准的车货匹配。然而,现有的车货匹配方法大多都忽视了历史车货数据,更不会处理和分析这些数据。因此,文章考虑到深度学习在数据处理上的优势,于是将深度学习和车货匹配方法相结合,提出了一种考虑注意力机制并基于SENet双塔模型的司机点击率预测模型——A-SENet双塔模型。具体来讲,就在双塔模型的大框架下,一方面利用SENet计算货物隐向量;另一方面利用Attention机制和SENet计算司机隐向量,并通过进一步计算得出司机点击货物的概率。通过某车货匹配平台的数据集进行实验,结果表明与基准模型相比,文章所提出的模型A-SENet双塔模型具有更好的性能,这不仅验证了利用深度学习进行车货匹配的可行性,而且表明注意力机制和SENet的使用有助于预测司机点击货物的概率。
There are large-scale historical vehicle-cargo data in the vehicle-cargo matching platform,through these historical data,drivers’ potential interests can be mined,which is helpful to predict the probability of drivers clicking on cargos(clickthrough rate prediction model of drivers),so as to recommend suitable cargos for drivers and realize accurate vehicle-cargo matching.However,most of the existing vehicle-cargo matching methods ignore the large-scale historical vehicle-cargo data,let alone consider how to process and analyze these data based on this.Therefore,considering the advantages of deep learning in data processing,combining deep learning with vehicle-cargo matching method,A-SENet two-tower model,a click-through rate prediction model of drivers based on SENet two-tower model considering attention mechanism is proposed.Specifically,under the framework of the two-tower model,on the one hand,SENet is used to calculate the hidden vector of cargos;on the other hand,attention mechanism and SENet are used to calculate the hidden vector of drivers and further calculate the probability of drivers clicking on cargos.Through the experiment on the data set of a vehicle-cargo matching platform,the results show that the proposed model A-SENet two-tower model has better performance than the benchmark model,which not only verifies the feasibility of using the deep learning method for vehicle-cargo matching,but also shows that the use of attention mechanism and SENet are helpful to predict the probability of drivers clicking on cargos.
作者
方芳
王成浩
FANG Fang;WANG Chenghao(School of Management,Hefei University of Technology,Hefei 230009,China)
出处
《物流科技》
2022年第10期91-97,共7页
Logistics Sci-Tech
基金
国家自然科学基金项目“科研社交网络中融合多源异构大数据的科研兴趣图谱和智能推荐研究”(91646111)
安徽省科技重大专项项目“指令级智能化深度检测工控防水墙关键技术及设备研发”(JZ2022AKKZ0015)。
关键词
车货匹配
点击率预测
双塔模型
注意力机制
SENet
vehicle-cargo matching
click-through rate prediction
two-tower model
attention mechanism
SENet
