Content-based Fashion Recommender System Using Unsupervised Learning

• Main Authors: Guillermo, Marielet, Espanola, Jason, Billones, Robert Kerwin, Vicerra, Ryan Rhay, Bandala, Argel, Sybingco, Edwin, Dadios, Elmer P., Fillone, Alexis
• Format: Conference Proceeding
• Language: English
• Subjects: Data mining; digital signal processing; Electronic commerce; feature vector; Footwear; image processing; Image reconstruction; latent-space representation; Pandemics; Signal processing; Training; unsupervised neural network
• ISSN: 2159-3450
• DOI: 10.1109/TENCON54134.2021.9707459

Data mining today is much slower than before because of the advancement of computing and information systems. Relevant recommendation based on customers' preferences and needs in e-commerce gets more complicated. In the recent pandemic, people are reluctant to go out and has engaged more on internet to get their daily food and services. This phenomenon exacerbated the existing recommendation system, as the data has grown up drastically. In this study, the author recommends a relevant image quality based on the quality queries of the clothes and footwear dataset by observing their highest similarity score. Fashion MNIST images used were existing dataset for clothes and footwear. The testing on image reconstruction using training and validation approaches has shown an accurate result by showing only 0.01 loss in the dataset. Using 11 classes of the image queries, the system image has been identically reconstructed according to the queries supplied. With this result, businesses will have an implementation alternative to a faster and more efficient data mining method. Hence, this alternative will boost the speed of many recommendation systems in the e-commerce platforms and will create a better customer experience.