Workshop on Recommender Systems in Fashion

Worldwide (online), 26th September 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. 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, Ralf Herbrich, Senior Vice President Data Science and Machine Learning at Zalando

Ralf Herbrich leads a diverse range of departments and initiatives that have, at their core, research in the area of artificial intelligence (AI) spanning data science, machine learning and economics in order for Zalando to be the starting point for fashion AI. Ralf’s teams apply and advance the science in many established scientific fields including computer vision, natural language processing, data science and economics. Ralf joined Zalando SE as SVP Data Science and Machine Learning in January 2020.
His research interests include Bayesian inference and decision making, natural language processing, computer vision, learning theory, robotics, distributed systems and programming languages. Ralf is one of the inventors of the Drivatars™ system in the Forza Motorsport series as well as the TrueSkill™ ranking and matchmaking system in Xbox Live.

Keynote Speaker, James Caverlee, Professor at Texas A&M University

James Caverlee is Professor and Lynn '84 and Bill Crane '83 Faculty Fellow at Texas A&M University in the Department of Computer Science and Engineering. His research targets topics from recommender systems, social media, information retrieval, data mining, and emerging networked information systems. His group has been supported by NSF, DARPA, AFOSR, Amazon, Google, among others. Caverlee serves as an associate editor for IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Intelligent Systems, and Social Network Analysis and Mining (SNAM). He was general co-chair of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), and has been a senior program committee member of venues like KDD, SIGIR, SDM, WSDM, ICWSM, and CIKM.​

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.)

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 present work evaluated in at least one of the described open datasets will be considered for the best paper, best student paper and best demo awards, which will be given by our sponsors.

Mentorship

For the first time, we will offer mentorship opportunities to students who would like to get initial feedback on their work by industry colleagues. We aim to increase the chances of innovative student’s work being published, as well as to foster an early exchange across academia and industry. As a mentee, you should expect at least one round of review of your work p r to the submission deadline. If your work is accepted, you should also expect at least one feedback session regarding your demo, poster or oral presentation.

If you want to participate in the mentorship program, please get in touch via e-mail.

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 29th, 2019.
  • Submissions should be prepared according to the a single-column 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). 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 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. Moreover, we are currently in conversations with Springer in order to publish the workshop papers in a special issue journal.

Additional Submission Instructions for Demos

The description of the demo should be prepared according to the standard double-column ACM SIG proceedings format with a one page limit. The submission should include:

  • 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)
  • Mentorship deadline: June 10th, 2020
  • Submission deadline: July 29th, 2020
  • Review deadline: August 14th, 2020
  • Author notification: August 21st, 2020
  • Camera-ready version deadline: September 4th, 2020
  • Workshop: September 26th, 2020
  • All deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone.
  • The organizing comitte strongly advises workshop participants to register as soon as possible, within the early registration dates published in the ACM RecSys Website.
  • (14:00 - 14:15 GTM)
    Opening and Introductions: by the Program Committee
    (14:15 - 15:00 GTM)
    Keynote Talk: Ralf Herbrich
    (15:00 - 16:00 GTM) Paper Session: Fashion Understanding
    • [paper] [presentation] The importance of brand affinity in luxury fashion recommendations, by Diogo Goncalves, Liwei Liu, João Sá, Tiago Otto, Ana Magalhães and Paula Brochado
    • [paper] [presentation] Probabilistic Color Modelling of Clothing Items, Mohammed Al-Rawi and Joeran Beel
    • [paper] [presentation] User Aesthetics Identification for Fashion Recommendations, by Liwei Liu, Ivo Silva, Pedro Nogueira, Ana Magalhães and Eder Martins
    (16:00 - 16:30 GTM) Virtual Coffee Break
    (16:30 - 17:10 GTM) Paper Session: Size and Fit
    • [paper] [presentation] Attention Gets You the Right Size and Fit in Fashion, by Karl Hajjar, Julia Lasserre, Alex Zhao and Reza Shirvany
    • [paper] [presentation] Towards User-in-the-Loop Online Fashion Size Recommendation with Low Cognitive Load, by Leonidas Lefakis, Evgenii Koriagin, Julia Lasserre and Reza Shirvany
    (17:10 - 18:30) Panel Discussion : Different Perspectives on Fashion Recommendations
    • Heidi Woelfle (University of Minnesota, Wearable Technology Lab), Jessica Graves (Sefleuria), Julia Lasserre (Zalando), Paula Brochado (FarFetch), Shatha Jaradat (KTH Royal Institute of Technology)
    (18:30 - 19:00) Virtual Coffee Break
    (19:00 - 19:45) Keynote talk : James Caverlee on "Opinion Leaders in Fashion: Opportunities and Challenges for Recommender Systems"
    (20:00 - 21:00) Paper Session: Combining Fashion
    • [paper] [presentation] The Ensemble-Building Challenge for Fashion Recommendation: Investigation of In-home Practices and Assessment of Garment Combinations, by Jingwen Zhang, Loren Terveen, Lucy Dunne
    • [paper] [presentation] Outfit Generation and Recommendation - An Experimental Study, by Marjan Celikik, Matthias Kirmse, Timo Denk, Pierre Gagliardi, Duy Pham, Sahar Mbarek and Ana Peleteiro Ramallo
    • [paper] [presentation] Understanding Professional Fashion Stylists' Outfit Recommendation Process: A Qualitative Study, by Bolanle Dahunsi and Lucy Dunne
    (21:00 - 21:30)  Virtual Coffee Break
    (21:30 - 21:45 )  Shaping the Future of #FASHIONXRECSYS: A Collective Exercise
    (21:45 - 22:00)  Closing remarks

    Shatha Jaradat

    KTH Royal Institute of Technology

    Nima Dokoohaki

    Accenture AI

    Humberto Corona

    Booking.com

    Reza Shirvany

    Zalando

  • Ala Eddine Ayadi (LVMH)
  • Ana Peleteiro (Zalando)
  • Arushi Prakash (Amazon)
  • Cristina Gena (Università di Torino)
  • Diogo Goncalves (Farfetch)
  • Evgenii Koriagin (Zalando)
  • Gordon Blackadde (ASOS)
  • Hosna Sattar (Zalando)
  • James Caverlee (Texas A&M university)
  • Jelle Stienstra (PTTRNS.AI)
  • Julia Laserre (Zalando)
  • Katrien Laenen (KU Leuven)
  • Leonidas Lefakis (Zalando)
  • Marjan Celikik (Zalando)
  • Mirela Riveni (Independent researcher)
  • Nour Karessli (Zalando)
  • Ricardo Savii (Dafiti Group)
  • Roberto Roverso (Zalando)
  • Simon Walk (Detego)
  • Sofiede Cnudde (Asos)
  • Steve Essinger (Nike)
  • Steven Bourke (Zalando)
  • Zeno Gantner (Zalando)
  • The following is a non-exhaustive list of datasets that are relevant for the fashionXrecsys workshop. Participants presenting work in any of these datasets will automatically be part of the workshop's challenge track. If there is a public dataset that you think should be added to the list, please contact the organizing comittee.

    • Clothing Fit Dataset for Size Recommendation : https://www.kaggle.com/rmisra/clothing-fit-dataset-for-size-recommendation

      Product size recommendation and fit prediction are critical in order to improve customers’ shopping experiences and to reduce product return rates. However, modeling customers’ fit feedback is challenging due to its subtle semantics, arising from the subjective evaluation of products and imbalanced label distribution (most of the feedbacks are "Fit"). These datasets, which are the only fit related datasets available publically at this time, collected from ModCloth and RentTheRunWay could be used to address these challenges to improve the recommendation process.

    • Large-scale Fashion (DeepFashion): http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html

      Description: DeepFashion is a large-scale clothes database which contains over 800,000 diverse fashion images ranging from well-posed shop images to unconstrained consumer photos. DeepFashion is annotated with rich information of clothing items. Each image in this dataset is labeled with 50 categories, 1,000descriptive attributes, bounding box and clothing landmarks. DeepFashion also contains over 300,000 cross-pose/cross-domain image pairs.

    • DeepFashion2 dataset: https://github.com/switchablenorms/DeepFashion2

      Description: DeepFashion2 is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both commercial shopping stores and consumers. It totally has 801K clothing clothing items, where each item in an image is labeled with scale, occlusion, zoom-in, viewpoint, category, style, bounding box, dense landmarks and per-pixel mask.There are also 873K Commercial-Consumer clothes pairs.

    • Street2Shop : http://www.tamaraberg.com/street2shop/

      Description: Street2Shop has 20,357 labeled images of clothing worn by people in the real world, and 404,683 images of clothing from shopping websites. The dataset contains 39,479 pairs of exactly matching items worn in street photos and shown in shop images.

    • Fashionista: http://vision.is.tohoku.ac.jp/~kyamagu/research/clothing_parsing/

      Description: Fashionista is a novel dataset to study clothes parsing, containing 158,235 fashion photos with associated text annotations.

    • Paperdoll: http://vision.is.tohoku.ac.jp/~kyamagu/research/paperdoll/

      Description: The Paper Doll dataset is a large collection of tagged fashion pictures with no manual annotation. It contains over 1 million pictures from chictopia.com with associated metadata tags denoting characteristics such as color, clothing item, or occasion.

    • Fashion MNIST: https://github.com/zalandoresearch/fashion-mnist

      Description: Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.

    • ModaNet dataset: https://github.com/eBay/modanet

      Description: ModaNet is a street fashion images dataset consisting of annotations related to RGB images. ModaNet provides multiple polygon annotations for each image.

    • iMaterialist-Fashion: https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6

      Description: The dataset contains over 50K clothing images labeled for fine-grained segmentation.

    • women's e-commerce dataset: https://github.com/NadimKawwa/WomeneCommerce

      Description: This is a Women’s Clothing E-Commerce dataset revolving around the reviews written by customers. Its nine supportive features offer a great environment to parse out the text through its multiple dimensions. Because this is real commercial data, it has been anonymized, and references to the company in the review text and body have been replaced with “retailer”.

    • Amazon Reviews dataset: http://jmcauley.ucsd.edu/data/amazon/links.html

      Description: This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

    • Fashion Product Images Dataset: https://www.kaggle.com/paramaggarwal/ fashion-product-images-dataset

      Description: In addition to professionally shot high resolution product images, the dataset contains multiple label attributes describing the product which was manually entered while cataloging. The dataset also contains descriptive text that comments on the product characteristics.

    • Brazilian E-Commerce Public Dataset by Olist: https://www.kaggle.com/olistbr/brazilian-ecommerce#olist_products_dataset.csv

      Description: The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allows viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers. The dataset contains real commercial data, it has been anonymised, and references to the companies and partners in the review text have been replaced with the names of Game of Thrones great houses.

    • Flipkart products dataset: https://www.kaggle.com/PromptCloudHQ/flipkart-products

      Description: This is a pre-crawled dataset, taken as subset of a bigger dataset (more than 5.8 million products) that was created by extracting data from Flipkart.com, a leading Indian eCommerce store.

    • Fashion Takes Shape: https://www.groundai.com/project/fashion-is-taking-shape-understanding-clothing-preference-based-on-body-shape-from-online-sources/1

      Description: The dataset includes more than 18000 images with meta-data including clothing category, and a manual shape annotation indicating whether the person’s shape is above average or average. The data comprises 181 different users from chictopia. Using our multi-photo method, we estimated the shape of each user. This allowed us to study the relationship between clothing categories and body shape. In particular, we compute the conditional distribution of clothing category conditioned on body shape parameters.