Prof. Calvin Wong is currently the CEO and Centre Director of Laboratory for Artificial Intelligence in
Design (AiDLab), a research platform jointly established by the Hong Kong Polytechnic University (PolyU) and Royal College of Art
(RCA) in the UK, and is funded by the HKSAR Government.
Ranked among the world's top 2% of most-cited scientists in the field of “Artificial intelligence & Image Processing”, he
has been bridging academia and industry with his cutting-edge research. His success in AI applications is supported by his work
derived from his publications of more than 170 high impact journals in SCI journals including influential publications such as
IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Image Processing,
etc.
In 2019, he worked with Alibaba Group to establish the first-of-its-kind “Fashion AI Dataset”, which improves the accuracy of online
searches for fashion images impacting the shopping experiences of millions of users on the platform. Calvin’s innovation “AI based
textile material inspection system” has received numerous accolades including two Grand Awards and a Gold Medal with the
Congratulations at the 47th International Exhibition of Inventions in Geneva.
In 2022, his team developed the world’s first designer-led AI system, AiDA which had garnered global media attention from
BBC News, Vogue, MIT Technology Review, and many more. AiDA being featured in WGSN Big Ideas Report 2025 (Fashion) demonstrates
its influential role in the future development of fashion industry.
Fifth Workshop on Recommender Systems in Fashion
on September 18th at 2pm Singapore time
Held at the 17th ACM Conference on Recommender Systems, 18th-22nd September 2023, Singapore
Workshop contact
Accepted papers can be found under Program
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. Recommender Systems are often used to solve different complex problems in this domain, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations or size and fit recommendations. The fifth edition of this workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce with a particular focus on pandemic and post-pandemic era events and their short and long lasting effects on e-commerce and Fashion.
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 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.
Accepted papers
Main track:- [Dolev et al.] Efficient Large-Scale Visual Representation Learning and Evaluation
- [Desaki et al.] Automated Material Properties Extraction For Enhanced Beauty Product Discovery and Makeup Virtual Try-on
- [Candeias et al.] Tailor: Size Recommendations for High-End Fashion Marketplaces
- [Mallea et al.] Overcoming popularity bias in adversarial pairwise learning for fashion recommendations
*** The organizing committee of Fashion x RecSys 2023 acknowledges this paper as an exemplary piece of work, distinguished for its high quality among all the accepted submissions. - [Shilova et al.] AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting
- [Dibak et al.] UNICON: A unified framework for behavior-based consumer segmentation in e-commerce
- [Jaiswal et al.] Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience
- [Mbarek et al.] Exploring Fit and Shape for Predicting Garment Size Issues in Fashion with Multi-Task Learning
Program (Singapore time)
Keynotes
Tian Su is currently Vice-President eCommerce Platform at Zalando. Tian is an executive specializing in strategic transformation, and innovation. With a career trajectory originating from the scientific domain and traversing an array of industries, from medical research and banking to capital markets, large-scale retail/eCommerce, and fashion, Tian's expertise lies in data, AI/ML, and emerging technologies. At Zalando, Tian leads a team of technologists, innovating and adapting to meet evolving customer needs. They oversee departments focused on Personalisation, Discovery (Recommendation, Search, Browse, Zalando Fashion Assistant), Onsite Customer Behaviour Analytics, as well as Research team for Computer Vision and 3D model generation.
Call for Contributions
Suggested topics for submissions are (but not limited to):
- Pandemic era short on long term effects and potential solutions in Fashion recommendation systems)
- 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.)
Reproducibility
In order to encourage the reproducibility of research work presented in the workshop, we put together a list of open datasets in this website.Paper Submission Instructions
- All submissions and reviews will be handled electronically via EasyChair.
- Submissions should be prepared according to the a single-column ACM RecSys format . We will consider both long and short papers. For long papers, the maximum length is 16 pages including appendices (plus up to 2 pages references). For short papers, the maximum length is 8 pages (plus up to 2 pages references).
- 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, refer to your previous work in the third person 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 there is no implication that the authors are currently affiliated with the mentioned organization.
- 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 be registered and attend the workshop to present their work, either in-person or virtually. 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
Submissions for extended abstract of demos should be prepared according to the standard double-column ACM SIG proceedings format as described in the RecSys 2023 Demos Call for Contributions . 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. The maximum duration of screen capture is 10 minutes. (This section will be removed for the camera-ready version of accepted contributions but might be included in the virtual platform used to host the online part of the workshop.)
- We also highly encourage any external material related to the demo (e.g., shared code on GitHub). Please, provide a link to the shared code in the extended abstract accompanying the demo.
Datasets
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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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.
Scientific Committee
Fourth Workshop on Recommender Systems in Fashion, 2022: Held in Online (Worldwide)
- Workshop Webpage (ACM RecSys 2022 Page)
- The proceedings of the workshop were published in Recommender Systems in Fashion and Retail , by Humberto Jesús Corona Pampín and Reza Shirvany. Part of the Springer's Lecture Notes in Electrical Engineering book series (LNEE, volume 981)
Third Workshop on Recommender Systems in Fashion, 2021: Held in Online (Worldwide)
- Workshop Webpage (ACM RecSys 2021 Page)
- The proceedings of the workshop were published in Recommender Systems in Fashion and Retail , by Nima Dokoohaki, Shatha Jaradat, Humberto Jesús Corona Pampín and Reza Shirvany. Part of the Springer's Lecture Notes in Electrical Engineering book series (LNEE, volume 830)
Second Workshop on Recommender Systems in Fashion, 2020: Held in Online (Worldwide)
- Workshop Webpage (ACM RecSys 2020 Page)
- The proceedings of the workshop were published in Recommender Systems in Fashion and Retail , by Nima Dokoohaki, Shatha Jaradat, Humberto Jesús Corona Pampín and Reza Shirvany. Part of the Springer's Lecture Notes in Electrical Engineering book series (LNEE, volume 734)
First Workshop on Recommender Systems in Fashion, 2019: Held in Copenhagen (Denmark)
- Workshop Webpage (ACM RecSys 2019 Page)
- Selected extended papers were published in Fashion Recommender Systems , Edited by Nima Dokoohaki. Part of the Springer's Lecture Notes in Social Networks(LNSN, Volume 8768)
Organizers
Julia Lasserre
Zalando SE, Germany
Contact: julia.lasserre@zalando.de
Nima Dokoohaki
Accenture Global AI, Germany
Reza Shirvany
Zalando SE, Germany