Fusion residual neural network-based product image recommendation system

Junyuan Zheng; Qifeng Zhu; Ming Lei; Min Sun

doi:10.1117/12.2684772

19 October 2023 Fusion residual neural network-based product image recommendation system

Junyuan Zheng, Qifeng Zhu, Ming Lei, Min Sun

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127093S (2023) https://doi.org/10.1117/12.2684772
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

In recent years, electronic commerce has emerged as one of the dominant modes of commodity trading, and online shopping platforms have become ubiquitous in the daily life. When searching for products of interest, consumers often employ image-based search to allow the platform to recommend corresponding products. To meet this demand, this paper designed a network model that employs the residual neural network ResNet101 for feature extraction of images under the deep learning framework TensorFlow. The experiments demonstrate that this network model can effectively achieve classification results for relevant images in e-commerce. Moreover, the paper conducted dataset optimization when training on ResNet101. The use of this network model enables efficient and accurate identification of submitted product images, which can be returned to users as recommended products in a manner similar to the submitted product images.

Citation Download Citation

Junyuan Zheng, Qifeng Zhu, Ming Lei, and Min Sun "Fusion residual neural network-based product image recommendation system", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127093S (19 October 2023); https://doi.org/10.1117/12.2684772

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