ix
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Copyright © 2020 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in
any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations
in printed reviews, without the prior permission of the publisher.
Compatibility Modeling: Data and Knowledge Applications for Clothing Matching
Xuemeng Song, Liqiang Nie, and Yinglong Wang
www.morganclaypool.com
ISBN: 9781681736686 paperback
ISBN: 9781681736693 ebook
ISBN: 9781681736709 hardcover
DOI 10.2200/S00952ED1V01Y201909ICR069
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON INFORMATION CONCEPTS, RETRIEVAL, AND SERVICES
Lecture #69
Series Editor: Gary Marchionini, University of North Carolina, Chapel Hill
Series ISSN
Print 1947-945X Electronic 1947-9468
Compatibility Modeling
Data and Knowledge Applications
for Clothing Matching
Xuemeng Song
Shandong University
Liqiang Nie
Shandong University
Yinglong Wang
Qilu University of Technology (Shandong Academic of Science)
SYNTHESIS LECTURES ON INFORMATION CONCEPTS, RETRIEVAL,
AND SERVICES #69
C
M
&
cLaypoolMorgan publishers
&
ABSTRACT
Nowadays, fashion has become an essential aspect of people’s daily life. As each outfit usually
comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper
outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is
good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in
recent years the number of online fashion-oriented communities, like IQON and Chictopia, as
well as e-commerce sites, like Amazon and eBay, has grown. e tremendous amount of real-
world data regarding people’s various fashion behaviors has opened a door to automatic clothing
matching.
Despite its significant value, compatibility modeling for clothing matching that assesses
the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse
and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) com-
prehensive compatibility modeling with the multi-modal feature variables is largely untapped;
(c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance
the interpretability of the compatibility modeling; and (e) how to model the user factor in the
personalized compatibility modeling. ese challenges have been largely unexplored to date.
In this book, we shed light on several state-of-the-art theories on compatibility modeling.
In particular, to facilitate the research, we first build three large-scale benchmark datasets from
different online fashion websites, including IQON and Amazon. We then introduce a general
data-driven compatibility modeling scheme based on advanced neural networks. To make use
of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a
novel knowledge-guided compatibility modeling framework. ereafter, to enhance the model
interpretability, we put forward a prototype-wise interpretable compatibility modeling approach.
Following that, noticing the subjective aesthetics of users, we extend the general compatibility
modeling to the personalized version. Moreover, we further study the real-world problem of
personalized capsule wardrobe creation, aiming to generate a minimum collection of garments
that is both compatible and suitable for the user. Finally, we conclude the book and present future
research directions, such as the generative compatibility modeling, virtual try-on with arbitrary
poses, and clothing generation.
KEYWORDS
compatibility modeling, clothing matching, interpretable modeling, knowledge dis-
tillation, preference modeling, personalized capsule wardrobe
xiii
Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Our Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Book Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2
Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 Dataset I for General Compatibility Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Dataset II for Personalized Compatibility Modeling . . . . . . . . . . . . . . . . . . . . . 7
2.3 Dataset III for Personalized Wardrobe Creation . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3
Data-Driven Compatibility Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2.1 Fashion Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2.2 Representation Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.3 Nonlinear Compatibility Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.4 Compatibility Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3.5 BPR-DAE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3.6 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4.1 Experiment Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4.2 Data Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.4.3 On Model Comparison (RQ1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
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