Fashion Recommender Systems. 1st ed. 2020

種類:

電子ブック

責任表示:

edited by Nima Dokoohaki

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2020

著者名:

• Dokoohaki, Nima.
• SpringerLink (Online service)

シリーズ名:

Lecture Notes in Social Networks ;

ISBN:

9783030552183 [3030552187]    

注記:

Part 1. Cold Start in Recommendations -- Chapter 1. Fashion Recommender Systems in Cold Start ( Mehdi Elahi) -- Part 2. Complementary and Session Based Recommendation -- Chapter 2. Enabling Hyper-Personalisation: Automated AdCreative Generation and Ranking for Fashion e-Commerce (Sreekanth Vempati) -- Chapter 3. Two-Stage Session-based Recommendations with Candidate Rank Embeddings (Jose Antonio Sanchez Rodrguez) -- Part 3. Outfit Recommendations -- Chapter 4. Attention-based Fusion for Outfit Recommendation (Katrien Laenen) -- Chapter 5. Outfit2Vec: Incorporating Clothing Hierarchical MetaData into Outfits’ Recommendation (Shatha Jaradat) -- Part 4. Sizing and Fit Recommendations -- Chapter 6. Learning Size and Fit from Fashion Images (Nour Karessli) -- Part 5. Generative Outfit Recommendation -- Chapter 7. Generating High-Resolution Fashion Model Images Wearing Custom Outfits (Gokhan Yildirim).
This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers 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 social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be a very useful guide for online shopping websites, providing

ローカル注記:

学内専用E-BOOKS (local access only)