Titlebook: Recommender Systems in Fashion and Retail; Proceedings of the T Nima Dokoohaki,Shatha Jaradat,Reza Shirvany Conference proceedings 2022 The

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1876-1100 tive 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)..978-3-030-94018-8978-3-030-94016-4Series ISSN 1876-1100 Series E-ISSN 1876-1119

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Intractable

SkillSF: In the Sizing Game, Your Size is?Your Skillngful information between article sizes and customer sizes. Through experimentations with real-life data, we demonstrate that our approach SkillSF is competitive with the state-of-the-art size recommendation models and offers valuable insights as to the underlying true size of customers and articles.

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Style-Based Interactive Eyewear Recommendationsction while interacting with high-level expression attributes such as delicateness and strongness. This article presents the customer journey, our pragmatic approach to the recommendation system using styling expert input to bypass a cold-start problem and implementation details.

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Conference proceedings 2022rt 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

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Using Relational Graph Convolutional Networks to Assign Fashion Communities to Usersis problem where we assign predefined fashion communities to users in an Ecommerce ecosystem for downstream tasks. We model our problem as a link prediction task in knowledge graphs with multiple types of edges and multiple types of nodes depicting the intricate Ecommerce ecosystems. We employ Relat

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SkillSF: In the Sizing Game, Your Size is?Your Skillskills thanks to ranking players through a score representing their “true” skill according to their performance in past games. Inspired by this approach, we propose to tackle the challenging problem of online size recommendations in fashion by reformulating a customer’s purchases as multiple sizing