OutfitNet: Fashion Outfit Recommendation with Attention-Based Multiple Instance Learning
Yusan Lin, Maryam Moosaei, Hao Yang
Abstract
Recommending fashion outfits to users presents several challenges. First of all, an outfit consists of multiple fashion items, and each user emphasizes different parts of an outfit when considering whether they like it or not. Secondly, a user’s liking for a fashion outfit considers not only the aesthetics of each item but also the compatibility among them. Lastly, fashion outfit data is often sparse in terms of the relationship between users and fashion outfits. Not to mention, we can only obtain what the users like, but not what they dislike.
Topics & Concepts
Computer scienceHuman–computer interactionWorld Wide WebMultimediaGenerative Adversarial Networks and Image SynthesisAesthetic Perception and Analysis3D Shape Modeling and Analysis