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. 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. Moreover, the research interest on this area is increasing, demonstrated by the success of the past four editions of the fashionXrecsys workshops 2019 1 2020 2 2021 3 2022 4. The fifth edition of the 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.

Amidst the rapid growth of fashion e-commerce, remote fitting of fashion articles remains a complex and challenging problem and a main driver of customers’ frustration. Despite the recent advances in 3D virtual try-on solutions, such approaches still remain limited to a very narrow – if not only a handful – selection of articles, and often for only one size of those fashion items. Other state-of-the-art approaches that aim to support customers find what fits them online mostly require a high level of customer engagement and privacy-sensitive data (such as height, weight, age, gender, belly shape, etc.), or alternatively need images of customers’ bodies in tight clothing. They also often lack the ability to produce fit and shape aware visual guidance at scale, coming up short by simply advising which size to order that would best match a customer’s physical body attributes, without providing any information on how the garment may fit and look. Contributing towards taking a leap forward and surpassing the limitations of current approaches, we present FitGAN, a generative adversarial model that explicitly accounts for garments’ entangled size and fit characteristics of online fashion at scale. Conditioned on the fit and shape of the articles, our model learns disentangled item representations and generates realistic images reflecting the true fit and shape properties of fashion articles. Through experiments on real world data at scale, we demonstrate how our approach is capable of synthesizing visually realistic and diverse fits of fashion items and explore its ability to control fit and shape of images for thousands of online garments.

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. 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. Moreover, the research interest on this area is increasing, demonstrated by the success of the past three editions of the fashionXrecsys Workshops 2019-21. The Fourth edition of the 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 era events and their short and long lasting effects on e-commerce and Fashion.

E-commerce is growing at an unprecedented rate and the fashion industry has recently witnessed a noticeable shift in customers' order behaviour towards stronger online shopping. However, fashion articles ordered online do not always find their way to a customer's wardrobe. In fact, a large share of them end up being returned. Finding clothes that fit online is very challenging and accounts for one of the main drivers of increased return rates in fashion e-commerce. Size and fit related returns severely impact 1. the customers experience and their dissatisfaction with online shopping, 2. the environment through an increased carbon footprint, and 3. the profitability of online fashion platforms. Due to poor fit, customers often end up returning articles that they like but do not fit them, which they have to re-order in a different size. To tackle this issue we introduce SizeFlags, a probabilistic Bayesian model based on weakly annotated large-scale data from customers. Leveraging the advantages of the Bayesian framework, we extend our model to successfully integrate rich priors from human experts feedback and computer vision intelligence. Through extensive experimentation, large-scale A/B testing and continuous evaluation of the model in production, we demonstrate the strong impact of the proposed approach in robustly reducing size-related returns in online fashion over~14~countries.

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. 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. Moreover, the research interest on this area is increasing, demonstrated by the success of the first and second editions of the fashionXrecsys Workshop in 2019-2020. The third edition of the workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce.

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. 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. Moreover, the research interest on this area is increasing, demonstrated by the success of the first edition of the fashionXrecsys Workshop in 2019. The second edition of the workshop aims at providing an avenue for continuing the discussion of novel approaches and applications of recommendation systems in fashion and e-commerce.

Fashion e-commerce has enjoyed an exponential growth in the last few years. A key challenge of the market players is to offer customers a personalized experience and to suggest relevant articles. In that respect, although product recommendation is a well-studied field, size and fit recommendation is still in its infancy. The size and fit topic is a very challenging problem as data is extremely sparse and noisy. Most approaches so far have exploited traditional machine learning techniques. In this work, we bring forward a meta-learning approach using an underlying deep neural network. The advantage of such an approach lies in its ability to exploit large scale data, learn across fashion categories, and absorb new data efficiently without re-training. We benchmark our method against 3 recent methods proven successful in the domain, and demonstrate various strengths of the proposed approach. To that end, we use a large-scale anonymized dataset of about 9.4 million customer-size interactions, collected over 5 years from around 384k customers.

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. Recommender Systems are often used to solve different complex problems in this scenario, such as social fashion-aware recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. However, relatively little research has been done on these complex problems. The very First fashionXrecsys Workshop aims at addressing these issues by providing a avenue for discussing novel approaches to recommendations in fashion and e-commerce applications.

Personalized size and fit recommendations bear crucial significance for any fashion e-commerce platform. Predicting the correct fit drives customer satisfaction and benefits the business by reducing costs incurred due to size-related returns. Traditional collaborative filtering algorithms seek to model customer preferences based on their previous orders. A typical challenge for such methods stems from extreme sparsity of customer-article orders. To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation. Our proposed method can ingest arbitrary customer and article data and can model multiple individuals or intents behind a single account. The method optimizes a global set of parameters to learn population-level abstractions of size and fit relevant information from observed customer-article interactions. It further employs customer and article specific embedding variables to learn their properties. Together with learned entity embeddings, the method maps additional customer and article attributes into a latent space to derive personalized recommendations. Application of our method to two publicly available datasets demonstrate an improvement over the state-of-the-art published results. On two proprietary datasets, one containing fit feedback from fashion experts and the other involving customer purchases, we further outperform comparable methodologies, including a recent Bayesian approach for size recommendation.

We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion. Our approach jointly models a size purchased by a customer, and its possible return event: 1. no return, 2. returned too small 3. returned too big. Those events are drawn following a multinomial distribution parameterized on the joint probability of each event, built following a hierarchy combining priors. Such a model allows us to incorporate extended domain expertise and article characteristics as prior knowledge, which in turn makes it possible for the underlying parameters to emerge thanks to sufficient data. Experiments are presented on real (anonymized) data from millions of customers along with a detailed discussion on the efficiency of such an approach within a large scale production system.

Analysis and comparison of linear and hybrid/compact dual-polarization (dual-pol) synthetic aperture radar (SAR) imagery have gained a wholly new importance in the last few years, in particular, with the advent of new spaceborne SARs such as the Japanese ALOS PALSAR, the Canadian RADARSAT-2, and the German TerraSAR-X. Compact polarimetry, hybrid dual-pol, and quad-pol modes are newly promoted in the literature for future SAR missions. In this paper, we investigate and compare different hybrid/compact and linear dual-pol modes in terms of the estimation of the degree of polarization (DoP). The DoP has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. It can be effectively used to characterize the information content of SAR data. We study and compare the information content of the intensity data provided by different hybrid/compact and linear dual-pol SAR modes. For this purpose, we derive the joint distribution of multilook SAR intensity images. We use this distribution to derive the maximum likelihood and moment-based estimators of the DoP in hybrid/compact and linear dual-pol modes. We evaluate and compare the performance of these estimators for different modes on both synthetic and real data, which are acquired by RADARSAT-2 spaceborne and NASA/JPL airborne SAR systems, over various terrain types such as urban, vegetation, and ocean.

Synthetic Aperture Radar (SAR) is a powerful tool strongly employed in maritime monitoring and surveillance. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field. The performance of the DoP is studied for ship detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on C-band polarimetric data acquired by RADARSAT-2 over San Francisco Bay and the Strait of Gibraltar.

Information contained in polarimetric images can be characterized by a scalar parameter called the polarization degree. This parameter is usually estimated using four polarimetric images. However, acquisition and registration of four images are complex and costly. Thus, reducing the number of required images can be of great interest for practical applications. In coherent illumination, these images are degraded by speckle noise. This noise can be reduced by transforming the original single-look images into multi-look images. We provide maximum likelihood estimators of the polarization degree in the general case of multi-look intensity images. The estimators are derived based on only two measurements, under coherent illumination and fully developed speckle. We evaluate our method on synthetic data and compare its performance with that of moment-based methods.

Analysis of dual-polarized SAR imagery has gained new importance with the recent launches of ALOS PALSAR, RADARSAT-2, and TerraSAR-X polarimetric SAR systems. Information contained in polarimetric images, collected by these SAR systems, can be characterized by a scalar parameter called the degree of polarization. This parameter has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. In this paper, we provide maximum likelihood and moment-based estimators of the degree of polarization in dual-polarized SAR imagery. We evaluate and compare the performance of these estimators on RADARSAT-2 polarimetric data, over various terrain types such as urban, vegetation, and ocean.

The degree of polarization (DoP) has long been recognized as one of the most important parameters characterizing partially polarized electromagnetic waves. This parameter can be effectively used to describe the information content of polarimetric images collected by synthetic aperture radar (SAR) systems. Estimation of DoP is standardly performed using four measurements. In SAR compact polarimetry (CP), however, only two measurements are available. In this paper, we develop maximum likelihood estimators of the DoP, in SAR CP modes, based on only two intensity images. We evaluate and compare the performance of these estimators for different CP modes on RADARSAT-2 polarimetric data, over various terrain types such as urban, vegetation, and ocean.