Product selection method based on feature level fusion of heterogeneous product data

To enable e-commerce sellers to identify products favoured by consumer groups and thereby increase their store's sales and profit margins, a product classification model based on feature-level fusion of heterogeneous data is proposed. The model uses a convolutional neural network to learn feature representations from product title, descriptions and reviews. It also uses a deep autoencoder to learn feature representations from product attributes. By combining the features of heterogeneous data, a comprehensive representation of the product is obtained. Experiments were conducted using data from all basketball products on the Amazon e-commerce platform. The results of the experiments showed that building a product classification model based on heterogeneous data outperformed using only product title. The accuracy of the model improved by 3.13% and the F1 score increased by 4.54%. These results strongly suggest that the product classification model based on heterogeneous data has learned more comprehensive features related to product popularity.