The article is devoted to the application of language neural networks for matching text descriptions of products. The analysis of methods for comparing text descriptions of products is carried out, the advantages and disadvantages of each method are noted. The method for matching text descriptions of products, based on Bert neural networks, is considered. Experiments and tests on data sets of text descriptions of similar goods from different retail chains are carried out. Conclusions about the quality of matching various networks of the Bert architecture are made.

Keywords: neural networks, transformers, comparison of text descriptions, text analysis, Bert

The article focuses on the use of convolutional neural networks for matching product images. The importance of developing systems for products image matching is described. The analysis of image comparison methods is carried out, the advantages and disadvantages of each method are noted. The comparison of matching products images from retailers using the ResNet neural network has been executed. Experiments and testing on datasets of products from retailers for image matching have been carried out with ResNet neural networks, the accuracy of image matching for different architectures of the ResNet neural network has been examined. Conclusions about the possibility of using the ResNet neural networks for matching product images were made.

Keywords: convolutional neural network, recognition, image, image analysis, image matching, ResNet

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