Editors:
- Offers the best contributions of the 2nd workshop on recommender systems in fashion and retail, held in 2020
- Presents a state-of-the-art view of recommender systems for e-commerce
- Provides readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field
Part of the book series: Lecture Notes in Electrical Engineering (LNEE, volume 734)
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Table of contents (8 papers)
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Front Matter
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Sizing and Fit in Online Fashion
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Front Matter
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Combining Fashion
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Front Matter
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About this book
This book includes the proceedings of the second workshop on recommender systems in fashion and retail (2020), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. Recommender systems are often used to solve different complex problems in this scenario, such as product recommendations, or size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).
Editors and Affiliations
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KTH - Royal Institute of Technology, Stockholm, Sweden
Nima Dokoohaki, Shatha Jaradat
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Machine Learning Platform, Booking.com, Amsterdam, The Netherlands
Humberto Jesús Corona Pampín
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Digital Experience-AI & Builder Platform, Zalando SE, Berlin, Germany
Reza Shirvany
Bibliographic Information
Book Title: Recommender Systems in Fashion and Retail
Editors: Nima Dokoohaki, Shatha Jaradat, Humberto Jesús Corona Pampín, Reza Shirvany
Series Title: Lecture Notes in Electrical Engineering
DOI: https://doi.org/10.1007/978-3-030-66103-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-66102-1Published: 24 March 2021
Softcover ISBN: 978-3-030-66105-2Published: 24 March 2022
eBook ISBN: 978-3-030-66103-8Published: 23 March 2021
Series ISSN: 1876-1100
Series E-ISSN: 1876-1119
Edition Number: 1
Number of Pages: V, 160
Number of Illustrations: 7 b/w illustrations, 45 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence, Computational Intelligence, e-Commerce/e-business, Structural Materials, Computer Imaging, Vision, Pattern Recognition and Graphics