Online fashion marketplaces are experiencing a boost in popularity. People see the appeal of websites where they can sell their products by providing information such as title, price, description, and pictures. With this popular new model for buying and selling fashion products comes a new set of challenges to face. With attention focused on analyzing product titles provided by the user, this paper covers the application of natural language processing techniques and a couple of machine learning algorithms to an online fashion marketplace, with the goal of predicting an item's category or subcategory. The paper begins with an overview of some popular preprocessing techniques in the context of analyzing titles. These preprocessing techniques are vital to the next step, the actual training of the models. This paper covers the development and performance of two models: a model that utilizes a Nave Bayesian learning approach, and a model that utilizes Support Vector Machines as the prediction model. The results from each prediction model are compared and discussed. The results show that the prediction model that utilized the Support Vector Machines was more accurate, and that natural language processing techniques can be effectively applied to an online fashion marketplace to predict an item's category or subcategory.
Kendal Norman, Zhi Li, Young-Taek Oh, Gautam Golwala, Sathya Sundaram, Jan Allebach, "Application of natural language processing to an online fashion marketplace" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, 2018, pp 444-1 - 444-5, https://doi.org/10.2352/ISSN.2470-1173.2018.10.IMAWM-444