16th ACM Conference on Recommender Systems,
Seattle, WA, USA, 18th-23rd September 2022
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.
| Invited talk: The FarFetch dataset at SIGIR e-com Invited talk: The Dressipi dataset at ReSysChallenge 2022 |
| [preprint] Adversarial Attacks against Visually-aware Fashion Outfit Recommender Systems, by Matteo Attimonelli, Gianluca Amatulli, Leonardo Di Gioia, Daniele Malitesta, Yashar Deldjoo and Tommaso Di Noia. |
| [preprint] Identification of Fine-grained Fit Information from Customer Reviews in Fashion, by Yevgeniy Puzikov, Sonia Pecenakova, Rodrigo Weffer, Leonidas Lefakis and Reza Shirvany. |
| [ preprint] (DEMO) A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail, by Jamie McGowan, Elizabeth Guest, Ziyang Yan, Cong Zheng, Neha Patel, Mason Cusack, Charlie Donaldson, Sofie de Cnudde, Gabriel Facini and Fabon Dzogang. |
| [preprint] Contrastive Learning for Topic-Dependent Image Ranking, by Jihyeong Ko, Jisu Jeong and Kyungmin Kim. |
| [preprint] Personalization through User Attributes for Transformer-based Sequential Recommendation, by Elisabeth Fischer, Alexander Dallmann and Andreas Hotho. |
| [preprint] (DEMO)VICTOR: Visual Incompatibility Detection with Transformers and Fashion-specific contrastive pre-training, by Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos and Ioannis Kompatsiaris. |
| [preprint] End-to-End Image-Based Fashion Recommendation, by Shereen Elsayed, Lukas Brinkmeyer and Lars Schmidt-Thieme. |
| [preprint] Reusable Self-Attention-based Recommender System for Fashion, by Marjan Celikik, Jacek Wasilewski, Sahar Mbarek, Pablo Celayes, Pierre Gagliardi, Duy Pham, Nour Karessli and Ana Peleteiro Ramallo.. |
Spotify, The Netherlands.
Zalando, Germany
This year we will continue to 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.
To be announced soon
Submissions for extended abstract of demos should be prepared according to the standard double-column ACM SIG proceedings format as described in the RecSys 2021 Demos Call for Contributions. The submission should include:
All deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone.
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 committee.
Amazon Reviews dataset:introduced by McAuley et al. contains product reviews and metadata from Amazon, including products in the Clothing, Shoes and Jewelry category. It includes 142.8 million reviews, ratings, product metadata as well as bought and viewed actions at product level. This dataset can be used in the tasks of single item recommendation or similarity calculation.
Large-scale Fashion (DeepFashion) is a fashion dataset built by Lui et. al, which contains a set of 800,000 diverse fashion images that can be used for different tasks such as fashion similarity, or fashion item recognition.
DeepFashion2 dataset is a dataset collected by Ge et. al, which contains 491,000 images, mapping to 801,000 clothing items and more detailed metadata associated with them. It has useful metadata information that can be used for tasks such as fashion similarity, fashion item recognition and single item recommendation.
Clothing Fit Dataset for Size Recommendation are datasets collected by Misra et al. from sever fashion e-commerce sites ModCloth and Rent The Runway. It contains size and self-reported fit information, as well as reviews and ratings for 1,738 items and 47,958 users. The dataset can be used for research in size and fit related recommendation problems.
ViBE (Dressing for Diverse Body Shapes) is a Dataset collected by [167] Hsiao et al. from the fashion site Birdsnest [168] which contains photography of gar- ments worn by a variety of models of different body shapes. The dataset also contains medatada for both the item and the model. Thus, this dataset is very interesting for solving size and fit related recommendation problems
Street2Shop is a dataset collected by M. Hadi Kiapour et al., which con- tains 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, which can be used for the task of similar item detection from images, complementary item recommendations, or outfit recommendations.
Pinterest’s Shop The Look Dataset collected by Kang et al. [140]. The dataset contains 47,739 scenes of people wearing fashion, which are labeled and linked to the corresponding 38,111 items. The dataset also has style labels, which makes it useful for the task of outfit recommendations or complementary item recom- mendations.
Alibaba iFashion is a dataset collected by Xu et al. The dataset contains 1.01 million outfits that are created by fashion experts. It also contains the clicks behaviors from 3.57 million users over a period of three months. This dataset can be useful for the task of outfits’ personalised recommendations.
PolyVore Outfits isis a dataset collected by Vasileva et al. It contains 68,306 outfits and 365,054 clothing items with their descriptions and types. This dataset is suitable for outfit recommendations or complementary item recommendations.