Humberto Jesús Corona Pampín,
Reza Shirvany
Springer Nature Switzerland
e druk, 2023
9783031221910
Recommender Systems in Fashion and Retail
Proceedings of the Fourth Workshop at the Recommender Systems Conference (2022)
Specificaties
Gebonden, blz.
|
Engels
Springer Nature Switzerland |
e druk, 2023
ISBN13: 9783031221910
Rubricering
Onderdeel van serie
Lecture Notes in Electrical Engineering
Levertijd ongeveer 8 werkdagen
Samenvatting
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).
Specificaties
ISBN13:9783031221910
Taal:Engels
Bindwijze:gebonden
Uitgever:Springer Nature Switzerland
Inhoudsopgave
Recommender Systems in Fashion and Retail: Proceedings of the Fourth<div>Workshop at the Recommender Systems Conference (2022) </div><div>Humberto Jes´us Corona Pamp´ın, Reza Shirvany</div><div><br></div><div>1. Identification of Fine-grained Fit Information from Customer Reviews in Fashion</div><div>Yevgeniy Puzikov, Sonia Pecenakova, Rodrigo Weffer, Leonidas Lefakis,Reza Shirvany</div><div>1.1 Introduction </div><div>1.2 Related Work</div><div>1.3 Experiments </div><div>1.4 Conclusion </div><div>References</div><div><br></div><div>2. Personalization through User Attributes for Transformer-based Sequential Recommendation</div><div>Elisabeth Fischer, Alexander Dallmann and Andreas Hotho</div><div>2.1 Introduction </div><div>2.2 Related Work </div><div>2.3 Problem Setting</div><div>2.4 User Attributes Personalization for Transformers </div><div>2.5 Datasets </div><div>2.6 Experiments </div>2.7 Conclusion <div>References </div><div><br></div><div>3. Reusable Self-Attention-based Recommender System for Fashion </div><div>Marjan Celikik, Jacek Wasilewski, Sahar Mbarek, Pablo Celayes, Pierre Gagliardi, Duy Pham, Nour Karessli, Ana Peleteiro Ramallo</div><div>3.1 Introduction </div><div>3.2 RELATED WORK </div><div>3.3 ALGORITHM</div><div>3.4 OFFLINE EVALUATION </div><div>3.5 ONLINE RESULTS </div><div>3.6 CONCLUSIONS</div><div>References </div><div><br></div><div>4. Adversarial Attacks against Visually-aware Fashion Outfit Recommender Systems </div>Matteo Attimonelli, Gianluca Amatulli, Leonardo Di Gioia, Daniele Malitesta, Yashar Deldjoo, Tommaso Di Noia<div>4.1 Introduction and Related work </div><div>4.2 Visual attacks against fashion classifiers </div><div>4.3 Experimental setup </div><div>4.4 Results and discussion </div><div>4.5 Conclusions </div><div>References</div><div> </div><div>5. Contrastive Learning for Topic-Dependent Image Ranking </div><div>Jihyeong Ko, Jisu Jeong and Kyumgmin Kim</div><div>5.1 Introduction </div><div>5.2 Related Work </div><div>5.3 Method </div><div>5.4 Experiments </div>5.5 Conclusion <div>References </div><div><br></div><div>6. Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail </div><div>Jamie McGowan, Elizabeth Guest, Ziyang Yan, Cong Zheng, Neha Patel, Mason Cusack, Charlie Donaldson, Sofie de Cnudde, Gabriel Facini and Fabon Dzogang</div><div>6.1 Introduction </div><div>6.2 Data Description </div><div>6.3 Methodology </div><div>6.4 Experiment Results </div><div>6.5 Conclusion </div><div><br></div><div>7. End-to-End Image-Based Fashion Recommendation </div><div>Shereen Elsayed, Lukas Brinkmeyer and Lars Schmidt-Thieme</div><div>7.1 Introduction </div><div>7.2 Related work </div><div>7.3 Methodology </div><div>7.4 Experiments </div><div>7.5 Conclusion </div><div>7.6 Acknowledgements </div><div>References </div><div><br></div>