Other Workshops in the Field

Workshop: FashionXRecsys

Saturday, September 26, 2020

Virtual

Event Details

FashionXRecsys - Workshop on Recommender Systems in Fashion

In conjunction with ACM RecSys 2020 (26th September 2020)

https://fashionxrecsys.github.io/fashionxrecsys-2020/

**Important Note** Due to concerns about COVID-19, RecSys 2020 will cancel its physical component and go fully virtual.

We are pleased to invite you to participate in the 2nd workshop on Recommender Systems in Fashion (FashionXRecsys) that will be held on September 26th, 2020.

Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout. However, the customers still face several hurdles with current online shopping solutions. For example, customers often feel overwhelmed with the large selection of the assortment and brands. In addition, there is still a lack of effective suggestions capable of satisfying customers’ style preferences, or size and fit needs, necessary to enable them in their decision-making process. In this context, recommender systems are very well positioned to play a crucial role in creating a great customer experience in fashion. Moreover, in recent years social shopping in fashion has surfaced, thanks to platforms such as Instagram, providing a very interesting opportunity that allows to explore fashion in radically new ways. Such recent developments provide exciting challenges for the Recommender Systems and Machine Learning research communities.

This workshop aims to bring together researchers and practitioners in the fashion, recommendations and machine learning domains to discuss open problems in the aforementioned areas. This involves addressing interdisciplinary problems with all of the challenges it entails. Within this workshop we aim to start the conversation among professionals in the fashion and e-commerce industries and recommender systems scientists, and create a new space for collaboration between these communities necessary for tackling these deep problems.