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Bebe Barrow <[log in to unmask]>
Mon, 18 Nov 2019 13:38:01 -0800
text/plain (95 lines)
I am pleased to announce the latest title in Morgan & Claypool's series on
Information Concepts, Retrieval, and Services:

 

Compatibility Modeling: Data and Knowledge Applications for Clothing
Matching

Xuemeng Song, Shandong University

Liqiang Nie, Shandong University

Yinglong Wang, Qilu University of Technology

Paperback ISBN: 9781681736686

eBook ISBN: 9781681736693

Hardcover ISBN: 9781681736709

November 2019, 138 pages

https://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?produ
cts_id=1474

 

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. The 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) comprehensive 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. These
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.
Thereafter, 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.

 

Table of Contents: Preface / Acknowledgments / Introduction / Data
Collection / Data-Driven Compatibility Modeling / Knowledge-Guided
Compatibility Modeling / Prototype-Wise Interpretable Compatibility Modeling
/ Personalized Compatibility Modeling / Personalized Capsule Wardrobe
Creation / Research Frontiers / Bibliography / Authors' Biographies

 

Series: Synthesis Lectures on Information Concepts, Retrieval, and Services

Editor: Gary Marchionini, University of North Carolina at Chapel Hill

http://www.morganclaypoolpublishers.com/catalog_Orig/index.php?cPath=22&sort
=2d&series=32


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