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FashionXRecsys - Workshop on Recommender Systems in Fashion
In conjunction with ACM RecSys 2020 (26th September 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 customersí 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. To provide rich opportunities to share opinions and experience in such an emerging field, we will accept paper submissions on established and novel ideas, as well as new interactive participation
Suggested topics for submissions are (but not limited to):
* Computer vision in Fashion (image classification, semantic segmentation, object detection)
* Deep learning in recommendation systems for Fashion
* Learning and application of fashion style (personalized style, implicit and explicit preferences, budget, social behaviour, etc)
* Size and Fit recommendations through mining customers implicit and explicit size and fit preferences
* Modelling articles and brands size and fit similarity
* Usage of ontologies and article metadata in fashion and retail (NLP, social mining, search)
* Addressing cold-start problem both for items and users in fashion recommendation
* Knowledge transfer in multi-domain fashion recommendation systems
* Hybrid recommendations on customersí history and on-line behavior
* Multi- or Cross- domain recommendations (social media and online shops)
* Privacy preserving techniques for customerís preferences tracing
* Understanding social and psychological factors and impacts of influence on usersí fashion choices (such as Instagram, influencers, etc.)
In order to encourage the reproducibility of research work presented in the workshop, we put together a list of open datasets in the fashionXrecsys
website. All submissions that present their work using at least one of the listed datasets will be considered for the best paper, best student paper and best demo awards, which will be given by workshop sponsors.
* Paper Submission deadline: July 29th, 2020
* Author notification: August 21st, 2020
* Camera-ready version deadline: September 4th,2020
All deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone.
Paper Submission Instructions
* Submissions should be prepared according to the ACM RecSys format. Long papers should report on
substantial contributions of lasting value. The maximum length is 14 pages (excluding references) in the new single-column format. For short papers, the maximum length is 7 pages (excluding references) in the new single-column format.
* The peer review process is double-blind (i.e. anonymised). All submissions must not include information identifying the authors or their organisation. Specifically, do not include the authorsí
names and affiliations, anonymise citations to your previous work and avoid providing any other information that would allow to identify the authors, such as acknowledgments and funding. However, it is acceptable to explicitly refer in the paper to the companies
or organizations that provided datasets, hosted experiments or deployed solutions, if specifically necessary for understanding the work described in the paper.
* Submitted work should be original. However, technical reports or ArXiv disclosure prior to or simultaneous with the workshop submission, is allowed, provided they are not peer-reviewed.
* The organizers also encourage authors to make their code and datasets publicly available.
* Accepted contributions are given either an oral or poster presentation slot at the workshop. At least one author of every accepted contribution must attend the workshop and present their work.
Please contact the workshop organization if none of the authors will be able to attend.
* All accepted papers will be available through the program website, and will be published
in a special Springer Journal issue.
Additional Submission Instructions for Demos
* An overview of the algorithm or system that is the core of the demo, including citations to any publications that support the work.
* A discussion of the purpose and the novelty of the demo.
* A description of the required setup. If the system will feature an installable component (e.g., mobile app) or website for users to use throughout or after the conference, please mention this.
* A link to a narrated screen capture of your system in action, ideally a video. (This section will be removed for the camera-ready version of accepted contributions.)
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