Graph Learning for Fashion Compatibility Modeling

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 provides 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.

Presents state-of-the-art theories to illustrate more challenging outfit compatibility modeling scenarios Discusses graph-learning based outfit compatibility models, which have been proven effective over real-world datasets Introduces fashion compatibility modeling to automatically justify the matching degree of complementary fashion items

Autorentext
Lei Zhu, Ph.D. is a Professor in the School of Information Science and Engineering, Shandong Normal University. He received his B.Eng. and Ph.D. degrees from Wuhan University of Technology and Huazhong University Science and Technology, respectively. He was a Research Fellow at the University of Queensland (2016-2017). His research interests include large-scale multimedia content analysis and retrieval. Jingjing Li, Ph.D, is a Professor in the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). He received his B.Eng., M.Sc. and Ph.D. degrees from UESTC. His research interests include domain adaptation and zero-shot learning. Weili Guan received a master degree from National University of Singapore. After that, she joined Hewlett Packard Enterprise in Singapore as a Software Engineer and worked there for several years. She is currently a PhD student with the Faculty of Information Technology, Monash University (Clayton Campus), Australia. Her research interests are multimedia computing and information retrieval. She has authored or co-authored more than 30 papers at first-tier conferences and journals, such as ACM MM, SIGIR, and IEEE TIP.

Inhalt
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.