Recommender Systems in Fashion and Retail

Proceedings of the Fourth Workshop at the Recommender Systems Conference (2022)
2023
ISBN: 978-3-031-22194-1
Verlag: Springer Nature Switzerland

Buch, Englisch, Band 981, 119 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 213 g

Reihe: Lecture Notes in Electrical Engineering

This book includes the proceedings of the fourth workshop on recommender systems in fashion and retail (2022), 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, size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Shirvany, Reza

Corona Pampín, Humberto Jesús

Fachgebiete

• Sozialwissenschaften Medien- und Kommunikationswissenschaften Kommunikationswissenschaften Digitale Medien, Internet, Telekommunikation
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Digital Lifestyle Internet, E-Mail, Social Media
• Interdisziplinäres Wissenschaften Wissenschaft und Gesellschaft | Kulturwissenschaften Populärkultur
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management E-Commerce, E-Business, E-Marketing
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz

Inhaltsverzeichnis

1. Identification of Fine-grained Fit Information from Customer Reviews in Fashion.- 2. Personalization through User Attributes for Transformer-based Sequential Recommendation.- 3. Reusable Self-Attention-based Recommender System for Fashion.- 4. Adversarial Attacks against Visually-aware Fashion Outfit Recommender Systems.- 5. Contrastive Learning for Topic-Dependent Image Ranking.- 6. A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail.- 7. End-to-End Image-Based Fashion Recommendation.