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
- Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management E-Commerce, E-Business, E-Marketing
- Mathematik | Informatik EDV | Informatik Digital Lifestyle Internet, E-Mail, Social Media
- Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz
- Sozialwissenschaften Medien- und Kommunikationswissenschaften Kommunikationswissenschaften Digitale Medien, Internet, Telekommunikation
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Interdisziplinäres Wissenschaften Wissenschaft und Gesellschaft | Kulturwissenschaften Populärkultur
Weitere Infos & Material
Recommender Systems in Fashion and Retail: Proceedings of the FourthWorkshop at the Recommender Systems Conference (2022) Humberto Jes´us Corona Pamp´in, Reza Shirvany
1. Identification of Fine-grained Fit Information from Customer Reviews in FashionYevgeniy Puzikov, Sonia Pecenakova, Rodrigo Weffer, Leonidas Lefakis,Reza Shirvany1.1 Introduction 1.2 Related Work1.3 Experiments 1.4 Conclusion References
2. Personalization through User Attributes for Transformer-based Sequential RecommendationElisabeth Fischer, Alexander Dallmann and Andreas Hotho2.1 Introduction 2.2 Related Work 2.3 Problem Setting2.4 User Attributes Personalization for Transformers 2.5 Datasets 2.6 Experiments 2.7 Conclusion References
3. Reusable Self-Attention-based Recommender System for Fashion Marjan Celikik, Jacek Wasilewski, Sahar Mbarek, Pablo Celayes, Pierre Gagliardi, Duy Pham, Nour Karessli, Ana Peleteiro Ramallo3.1 Introduction 3.2 RELATED WORK 3.3 ALGORITHM3.4 OFFLINE EVALUATION 3.5 ONLINE RESULTS 3.6 CONCLUSIONSReferences
4. Adversarial Attacks against Visually-aware Fashion Outfit Recommender Systems Matteo Attimonelli, Gianluca Amatulli, Leonardo Di Gioia, Daniele Malitesta, Yashar Deldjoo, Tommaso Di Noia4.1 Introduction and Related work 4.2 Visual attacks against fashion classifiers 4.3 Experimental setup 4.4 Results and discussion 4.5 Conclusions References 5. Contrastive Learning for Topic-Dependent Image Ranking Jihyeong Ko, Jisu Jeong and Kyumgmin Kim5.1 Introduction 5.2 Related Work 5.3 Method 5.4 Experiments 5.5 Conclusion References
6. Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail Jamie McGowan, Elizabeth Guest, Ziyang Yan, Cong Zheng, Neha Patel, Mason Cusack, Charlie Donaldson, Sofie de Cnudde, Gabriel Facini and Fabon Dzogang6.1 Introduction 6.2 Data Description 6.3 Methodology 6.4 Experiment Results 6.5 Conclusion
7. End-to-End Image-Based Fashion Recommendation Shereen Elsayed, Lukas Brinkmeyer and Lars Schmidt-Thieme7.1 Introduction 7.2 Related work 7.3 Methodology 7.4 Experiments 7.5 Conclusion 7.6 Acknowledgements References
