Buch, Englisch, 119 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 371 g
Proceedings of the Fourth Workshop at the Recommender Systems Conference (2022)
Buch, Englisch, 119 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 371 g
Reihe: Lecture Notes in Electrical Engineering
ISBN: 978-3-031-22191-0
Verlag: Springer Nature Switzerland
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.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Digital Lifestyle Internet, E-Mail, Social Media
- Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz
- Interdisziplinäres Wissenschaften Wissenschaft und Gesellschaft | Kulturwissenschaften Populärkultur
- Sozialwissenschaften Medien- und Kommunikationswissenschaften Kommunikationswissenschaften Digitale Medien, Internet, Telekommunikation
- Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management E-Commerce, E-Business, E-Marketing
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
Weitere Infos & Material
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.
