Based on the goal of achieving higher quality of instant delivery services,a model for instant delivery time prediction is constructed with multi-feature learning.Firstly,the impact of different fac-tors on the instant delivery process is fully considered,and multiple features are represented using ge-ographic hashing and graph embedding to facilitate the input of different models subsequently.Then,the association relationships between multiple features are learned by a combination of multi-head self-attention(MHSA)and residual connections,and the spatial relationships between delivery nodes are extracted using convolutional neural network(CNN),thus achieving sufficient learning and extraction of different features.Finally,the features extracted by different modules are fused and input to the multilayer perceptron module to realize the prediction of instant delivery time.The comparative exper-iments on real instant delivery dataset show that the proposed prediction model can effectively learn various features and association relationships,and the prediction effect is better.
维普
万方数据