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Graph Learning for Fashion Compatibility Modeling

Title
Graph Learning for Fashion Compatibility Modeling [electronic resource] / by Weili Guan, Xuemeng Song, Xiaojun Chang, Liqiang Nie.
ISBN
9783031188176
Edition
2nd ed. 2022.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2022.
Physical Description
1 online resource (XIV, 112 p.) 29 illus., 28 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios. In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling. Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years. Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items. This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets. This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items. To deal with the challenging task of outfit compatibility modeling, this book gives comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning. Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.
Variant and related titles
Springer Nature Synthesis Collection of Technology.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
November 17, 2022
Series
Synthesis Lectures on Information Concepts, Retrieval, and Services,
Synthesis Lectures on Information Concepts, Retrieval, and Services,
Contents
Introduction
Correlation-oriented Graph Learning for OCM
Modality-oriented Graph Learning for OCM
Unsupervised Disentangled Graph Learning for OCM
Supervised Disentangled Graph Learning for OCM
Heterogeneous Graph Learning for Personalized OCM
Research Frontiers.
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