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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.