Machine learning meets fashion

Data, algorithms and analytics for the fashion industry

14 August 2017, Halifax, Nova Scotia - Canada


Fashion is a multi-billion-dollar industry with social and economic implications worldwide. The fashion industry has traditionally placed high value on human creativity and has been slower to realize the potential of data analytics. With the advent of modern cognitive computing technologies (data mining and knowledge discovery, machine learning, deep learning, computer vision, natural language understanding etc.) and vast amounts of (structured and unstructured) fashion data the impact on fashion industry could be transformational. Already fashion e-commerce portals are using data to be branded as not just an online warehouse, but also as a fashion destination. Luxury fashion houses are planning to recreate physical in-store experience for their virtual channels, and a slew of technology startups are providing trending, forecasting, and styling services to fashion industry.

The second international workshop on fashion and KDD will be hosted at KDD 2017 in Halifax, Nova Scotia - Canada on 14th August, 2017. The goal of this workshop is to gather people from academia, industry, and startups working at the intersection of fashion and data mining and knowledge discovery to further the technology and its adoption. The first international workshop on fashion and KDD was organized at KDD 2016 and was a big success.

Topics of Interest

This is a new emerging area for the KDD community and we hope this workshop will bring together all the researchers, practitioners, and interested audiences to explore the open problems, applications, and future directions in this field. We believe that the fashion industry introduces a number of interesting data analytics problems that are either not studied or scarcely studied in the past and can attract great interest in the general KDD community given their practical implications. The first international workshop on fashion and KDD was organized at KDD 2016 and was a big success. Suggested topics include (but not limited to):

  • Detect and forecast fashion trends and cycles
  • Big data for fast fashion (the like of Zara, H&M, and Primark)
  • Analyzing fashion blogs, articles, and images
  • Visual search for fashion e-commerce
  • Fashion image understanding and auto-tagging of apparel
  • Novel search mechanisms for large fashion catalogs
  • Virtual personal fashion assistants
  • Recommendation engines and cognitive stylists for fashion
  • Balancing art and science in fashion recommendation algorithms
  • Personal styling with humans and machines: recommendations with humans in the loop
  • Assembling outfit recommendations: interactions and serendipity
  • Algorithmic clothing: design by data
  • Predicting fashionability scores
  • Social networking for fashion
  • Fashion retail analytics
  • Interactive textiles
  • Digital wardrobe
  • Mining style rules
  • Assessing fashion personality (from social media platforms)
  • Virtual trial rooms
  • Plagiarism detection in fashion
  • Fashion and wearable computing
  • Technology in fashion weeks

We also invite submissions in other retail domains where design, trends, styling, recommendations are important (for example, jewelry, furniture etc.).

Workshop Schedule

14th August, 2017 afternoon (1pm - 5pm)

1:00 - 1:30 pm - Welcome and invited talk by Kavita Bala on Fashion and Style Discovery: object and material recognition from online photo collections

1:30 - 2:30 pm - Oral Paper Presentations
  • 1:30 - 1:50 pm - Size Recommendation System for Fashion E-commerce
  • 1:50 - 2:10 pm - Learning Fashion Traits with Label Uncertainty
  • 2:10 - 2:30 pm - Sales Potential : Modeling Sellability of Fashion Product

2:30 - 3:00 pm - Invited talk by Madhu Kurup on Making fashion recommendations in cold start situations

3:00 - 3:30 pm - Coffee break & Poster Session

3:30 - 4:30 pm - Oral Paper Presentations
  • 3:30 - 3:50 pm - Cross-modal Search for Fashion Attributes
  • 3:50 - 4:10 pm - Algorithmic clothing: hybrid recommendation, from street-style-to-shop
  • 4:10 - 4:30 pm - Deciphering Fashion Sensibility Using Community Detection

4:30 - 5:00 - Panel discussion led by Menaka Sampath followed by an optional Open House

Invited Speakers

Kavita Bala
Professor, Cornell University

Madhu Kurup
Director of Amazon Personalization

Accepted Papers/Posters


Program Committee Members

  • Ranjitha Kumar, Assistant Professor, University of Illinois at Urbana-Champaign
  • Kavita Bala, Professor, Cornell University
  • Oleg Rybakov, Senior Machine Learning Scientist, Amazon
  • Ted Sandler, Senior Machine Learning Scientist, Amazon
  • Chen Fang, Research Scientist, Adobe Research
  • Ruining He, Doctoral Student, UC San Diego
  • Zheng zheng Xing, Machine Learning Scientist, Amazon
  • Priyanka Agrawal, Research Engineer, IBM Research
  • Ayushi Dalmia, Research Engineer, IBM Research
  • Hui Wu, Research Staff Member, IBM T. J. Watson Research Center

Submission Guidelines

We solicit submission of papers of 4 to 10 pages representing reports of original research, preliminary research results, case studies, proposals for new work and position papers. We also seek poster submissions based on recently published work (please indicate the conference published).

All papers will be peer reviewed, single blind (i.e. author names and affiliations should be listed). If accepted, at least one of the authors must attend the workshop to present the work. The submitted papers must be written in English and formatted in the double column standard according to the ACM Proceedings Template, Tighter Alternate style. The papers should be in PDF format and submitted via the EasyChair submission site. The workshop website will archive the published papers.

For more information or any clarifications please email

  • Paper Submission Deadline: May 26, 2017 extended June 2, 2017
  • Acceptance Notifications: June 16, 2017 extended June 20, 2017
  • Camera-Ready Submission Date: Jun 28, 2017
  • Workshop date: August 14, 2017

All deadlines are at 11:59 PM Pacific Standard Time.