Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …

The textile and apparel industries have grown tremendously over the past few years.
Customers no longer have to visit many stores, stand in long queues, or try on garments in …

Generative Adversarial Networks (GAN) have achieved big success in various domains
such as image generation, music generation, and natural language generation. In this …

This review explores the integration of enhanced personalization and seamless multimodal
interfaces in the field of fashion design and recommendation. We examine the increasing …

Fashion recommendation is a key research field in computational fashion research and has
attracted considerable interest in the computer vision, multimedia, and information retrieval …

Given an outfit, what small changes would most improve its fashionability? This question
presents an intriguing new computer vision challenge. We introduce Fashion++, an …

Real-world super platforms such as Google and WeChat usually have different
recommendation scenarios to provide heterogeneous items for users' diverse demands …

As handling fashion big data with Artificial Intelligence (AI) has become exciting challenges
for computer scientists, fashion studies have received increasing attention in computer …

The fashion industry is on the verge of an unprecedented change. The implementation of
machine learning, computer vision, and artificial intelligence (AI) in fashion applications is …

Body shape plays an important role in determining what garments will best suit a given
person, yet today's clothing recommendation methods take a" one shape fits all" approach …