Workshop on Recommender Systems in Fashion

Copenhagen, Denmark, 20th September 2019

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 provides 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 paper submissions on established and novel ideas, as well as new interactive participation formats.

Keynote Speaker, Stacia Carr (Zalando)

Stacia Carr is an American technology executive and startup co-founder. Throughout her career she’s led engineering teams developing consumer internet products and services with an emphasis on digital media distribution including SoundCloud, Sony, Listen.com, CMJ and Kink.com In 2013 she successfully negotiated the acquisition of the boutique consulting company she was running to Indiegogo. In 2014, she relocated from the US to Madrid, Spain, as the technical cofounder of Vidnex, a real-time video platform for the health and fitness industry. At Zalando, as Director of Engineering, she leads the Sizing team.

Keynote Speaker, Tamara Berg (Facebook AI)

Tamara Berg joined Facebook in January 2019 as a research scientist to work on computer vision related to people, fashion, and commerce. Her broader research interests span a variety of areas in computer vision and natural language processing, including developing models aimed at understanding and combining multiple modalities. Tamara Berg completed a PhD in computer science from the University of California, Berkeley in 2007, then worked as a research scientist at Yahoo! Research, and as a professor at Stony Brook University and UNC Chapel Hill (currently on leave). Most recently, She helped co-found Shopagon, a start-up focused on personalizing online shopping.

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 customers’ history and on-line behavior.
  • Multi- or Cross- domain recommendations (social media and online shops)
  • Privacy preserving techniques for customer’s preferences tracing.
  • Understanding social and psychological factors and impacts of influence on users’ fashion choices (such as Instagram, influencers, etc.)

Paper Submission Instructions

  • All submissions and reviews will be handled electronically via EasyChair Papers must be submitted by 23:59, AoE (Anywhere on Earth) on July 19th, 2019.
  • Submissions should be prepared according to the standard double-column ACM SIG proceedings format according to the standard double-column ACM SIG proceedings format. The ideal length of a paper is between 4-8 pages, but submissions have no strict page limits. Although the authors should avoid submitting unnecessarily long papers in order not to overwhelm reviewers.
  • The peer review process is double-blind (i.e. anonymised). This means that all submissions must not include information identifying the authors or their organisation. Specifically, do not include the authors’ 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, that 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 papers are given an oral and a poster presentation slot at the workshop. At least one author of every accepted paper must attend the workshop and present their work. Please contact the workshop organization if none of the authors will be able to attend the workshop.
  • All accepted papers will be available through the program website which will be linked from the official RecSys‘19 site. Moreover, selected authors will be invited to contribute an extended version of their paper to be published in a Journal.
  • Call for Papers Publication: March 25th 2019
  • Paper submission deadline: July 19th, 2019
  • Reviewer deadline: August 9th, 2019
  • Author notification: August 15th, 2019
  • Camera-ready version deadline: August 27th, 2019
  • Workshop: September 20th, 2019
All deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone.
(9:00 - 9:15)

Opening and Introductions: by the Program Committee

(9:15 - 10:00)

Keynote Talk: Tamara Berg (Facebook AI)

(10:00 - 10:30)

Session : Posters Lighting Talks

  • 10:00 - 10:05 [arXiv] Automated Fashion Size Normalization, by Eddie S.J. Du, Chang Liu and David H. Wayne.
  • 10:05 - 10:10 [arXiv] Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce, by Sreekanth Vempati, Sruthi V, Kora Malayil and Sandeep R.
  • 10:10 - 10:15 [arXiv] Supporting stylists by recommending fashion style, by Tobias Kuhn, Steven Bourke, Levin Brinkmann, Tobias Buchwald, Conor Digan, Hendrik Hache, Sebastian Jäger, Patrick Lehmann, Oskar Maier, Stefan Matting and Yura Okulovsky.
  • 10:15 - 10:20 [arXiv] How big can style be? Addressing high dimensionality for recommending with style, by Diogo Gonçalves, Liwei Liu and Ana Magalhães.
  • 10:20 - 10:25 [pdf] Analyzing Customer Feedback for Product Fit Prediction, by Stephan Baier.
(10:15 - 11:00) Coffee Break and Posters Presentation
(11:00 - 12:30)

Session: Outfits Recommendations and New Trends

  • 11:00 - 11:20 [pdf] Assessing Fashion Recommendations: A Multifaceted Offline Evaluation Approach, by Jake Sherman, Chinmay Shukla, Su Zhang, Rhonda Textor and Amy Winecoff.
  • 11:20 - 11:50 [arXiv] Attention-based Fusion for Outfit Recommendation, by Katrien Laenen and Marie-Francine Moens.
  • 11:50 - 12:10 Outfit2Vec: Incorporating Clothing Hierarchical MetaData intoOutfits’ Recommendation, by Shatha Jaradat, Nima Dokoohaki and Mihhail Matskin.
  • 12:10 - 12:30 [arXiv] Two-Stage Session-based Recommendations with Candidate Rank Embeddings, by Jose Antonio Sanchez Rodriguez, Jui-Chieh Wu and Mustafa Khandwawala.
(12:30 - 14:00) Lunch break
(14:00 - 14:45)

Keynote talk: Stacia Carr (Zalando)

(14:45 - 15:30)

Session: Complementary Items

  • 14:45 - 15:05 [arxiv] Session-based Complementary Fashion Recommendations, by Jui-Chieh Wu, Jose Antonio Sanchez Rodriguez and Humberto Jesus Corona Pampin.
  • 15:10 - 15:30 [arxiv] Complementary-Similarity Learning using Quadruplet Network, by Mansi Ranjit Mane, Stephen Guo and Kannan Achan.
(15:30 - 16:00) Coffee Break and Posters Presentation
(16:00 - 17:00)

Panel discussion: New Perspectives and Problems in Fashion Recommendation

(17:00 - 17:30)

Shaping the Future of #FASHIONXRECSYS: A Collective Exercise

(17:30 - 17:45)

Closing remarks

Shatha Jaradat

KTH Royal Institute of Technology

Nima Dokoohaki

Accenture AI

Humberto Corona

Zalando

Reza Shirvany

Zalando

  • Steven Bourke (Zapier)
  • Diogo Goncalves (Farfetch)
  • Leonidas Lefaki (Zalando)
  • Ana Peleteiro (Tendam)
  • Julia Lasserre (Zalando Research)
  • Mihhail Matskin (KTH Royal Institute of Technology, Sweden)
  • Ralf Krestel (University of Passau, Germany)
  • Soude Fazeli (Delft University of Technology, Netherlands)
  • Nitin Agarwal (University of Arkansas at Little Rock, USA)
  • Ralf Krestel (Hasso Plattner Institute of Technology, Germany)
  • Roberto Roverso (Zalando)
  • Simon Walk (Detego)
  • Saul Vargas (Asos)
  • Mirela Riveni (Independent researcher)
  • Federica Cena (Università degli Studi di Torino, Italy)
  • Szymon Chojnacki (Polish Academy of Sciences, Poland)