The online shopping experience for clothing can be further enhanced by implementing Deep Learning techniques, such as Computer Vision and personalized recommendation systems. Automation, as a principle, can be applied to solving problems surrounding efficacy, efficiency, and security. It also provides a layer of abstraction for the user during the online shopping experience. This research aims to apply Deep Learning methods and principles of automation to augment the e-commerce fashion market in a novel way. After using these methods, it was found that Convolutional Autoencoders and Item-to-Item Based Recommenders may be used to accurately and precisely recommend articles of clothing based on a users’ styling preferences.
Harris, Zachary O.; Katta, Gowtham G.; Slater, Robert; and Woodall, Joseph L. IV
"Deep Learning for Online Fashion: A Novel Solution for the Retail E-Commerce Industry,"
SMU Data Science Review: Vol. 6:
2, Article 17.
Available at: https://scholar.smu.edu/datasciencereview/vol6/iss2/17
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