Workshop on Recommender Systems in Fashion and Retail

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. 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 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. To provide rich opportunities to share opinions and experience in such an emerging field, we will accept papers on established and novel ideas, as well as new interactive participation formats such as demos. The workshop website includes a list of open datasets and is also offering a mentorship program for students.

Organizers
  • Shatha Jaradat, KTH Royal Institute of Technology
  • Nima Dokoohaki, KTH Royal Institute of Technology
  • Humberto Corona, Booking.com
  • Reza Shirvany, Zalando
Website

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

Date

14:0020:30

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