A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

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.Comment: Published at the Thirteenth ACM Conference on Recommender Systems (RecSys '19), September 16--20, 2019, Copenhagen, Denmar

SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images

Finding clothes that fit is a hot topic in the e-commerce fashion industry. Most approaches addressing this problem are based on statistical methods relying on historical data of articles purchased and returned to the store. Such approaches suffer from the cold start problem for the thousands of articles appearing on the shopping platforms every day, for which no prior purchase history is available. We propose to employ visual data to infer size and fit characteristics of fashion articles. We introduce SizeNet, a weakly-supervised teacher-student training framework that leverages the power of statistical models combined with the rich visual information from article images to learn visual cues for size and fit characteristics, capable of tackling the challenging cold start problem. Detailed experiments are performed on thousands of textile garments, including dresses, trousers, knitwear, tops, etc. from hundreds of different brands.Comment: IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) 2019 Focus on Fashion and Subjective Search - Understanding Subjective Attributes of Data (FFSS-USAD

Optimizing product recommendations for a try-before-you-buy fashion e-commerce sit

The fashion e-commerce market has experienced a significant growth and more and more customers tend to buy products online, rather than in physical stores. However, after a customer buys a product online, only a fraction of the garments stay in their wardrobe as many items are being returned to the vendor. Due to the absence of physical examination and misleading product descriptions customers struggle to find the right product suitable to their personal preferences. Especially the category of women’s lingerie suffers to a great extend from high return rates. Different sources report that between 70 up to 100% of women wear wrong sized bras. Personalized recommendations through so called recommendation systems play an essential role in e-commerce. This thesis aims to optimize the current product recommendations of a Belgium start-up called CurveCatch that sells women’s lingerie articles online and relies on a try-before-you-buy concept. To predict which products a customer is likely to buy two different personalized deep learning approaches were introduced. Data sparsity was addressed by labeling each unique product per customer and minority classes were synthetically oversampled. The findings demonstrated that recommendation systems are not only relevant for companies operating on a large scale. Rather, they also can be a valuable source of accurate recommendations for start-ups with sparse data. However, results also underlined well-known limitations of recommendation systems. Both models struggled especially when identifying products a customer is likely to buy, while it was rather easy to identify products a customer is not likely to buy.O mercado de e-commerce de moda experimentou um crescimento significativo e cada vez mais os clientes tendem a comprar produtos online, em vez de em lojas físicas. No entanto, muitos itens são devolvidos ao vendedor após a compra online, pois os clientes têm dificuldade em encontrar o produto certo adequado às suas preferências pessoais devido à falta de exame físico e às descrições de produtos enganosas. A categoria de lingerie feminina sofre muito com as altas taxas de devolução. Diferentes fontes relatam que entre 70% e 100% das mulheres usam sutiãs do tamanho errado. As recomendações personalizadas através dos chamados sistemas de recomendação desempenham um papel essencial no e-commerce. Esta tese visa otimizar as atuais recomendações de produtos de uma start-up belga chamada CurveCatch que vende artigos de lingerie feminina online e depende de um conceito de experimente antes de comprar. Para prever quais produtos um cliente é mais propenso a comprar, foram introduzidos dois diferentes abordagens de aprendizado profundo personalizadas. A escassez de dados foi abordada rotulando cada produto único por cliente e as classes minoritárias foram sobreamostradas sinteticamente. Os resultados demonstraram que os sistemas de recomendação também podem ser uma fonte valiosa de recomendação de produtos para start-ups com dados escassos. No entanto, os resultados também sublinharam as bem conhecidas limitações dos sistemas de recomendação. Ambos os modelos lutaram especialmente ao identificar os produtos que um cliente é mais propenso a comprar, enquanto era relativamente fácil identificar os produtos que um cliente não é propenso a comprar

Multimodal sequential fashion attribute prediction

We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, the sequential model is also better able to generate sequences of attribute chains not seen during training. We also measure the contributions of both image and textual input and show that while text-only models always outperform image-only models, only the multimodal sequential model combining both image and text improves over the text-only model on all experimental dataset

Computational Technologies for Fashion Recommendation: A Survey

Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation, have been posed and explored in the literature. The continuing research attention and advances impel us to look back and in-depth into the field for a better understanding. In this paper, we comprehensively review recent research efforts on fashion recommendation from a technological perspective. We first introduce fashion recommendation at a macro level and analyse its characteristics and differences with general recommendation tasks. We then clearly categorize different fashion recommendation efforts into several sub-tasks and focus on each sub-task in terms of its problem formulation, research focus, state-of-the-art methods, and limitations. We also summarize the datasets proposed in the literature for use in fashion recommendation studies to give readers a brief illustration. Finally, we discuss several promising directions for future research in this field. Overall, this survey systematically reviews the development of fashion recommendation research. It also discusses the current limitations and gaps between academic research and the real needs of the fashion industry. In the process, we offer a deep insight into how the fashion industry could benefit from fashion recommendation technologies. the computational technologies of fashion recommendation

Human Body Shape Classification Based on a Single Image

There is high demand for online fashion recommender systems that incorporate the needs of the consumer's body shape. As such, we present a methodology to classify human body shape from a single image. This is achieved through the use of instance segmentation and keypoint estimation models, trained only on open-source benchmarking datasets. The system is capable of performing in noisy environments owing to to robust background subtraction. The proposed methodology does not require 3D body recreation as a result of classification based on estimated keypoints, nor requires historical information about a user to operate - calculating all required measurements at the point of use. We evaluate our methodology both qualitatively against existing body shape classifiers and quantitatively against a novel dataset of images, which we provide for use to the community. The resultant body shape classification can be utilised in a variety of downstream tasks, such as input to size and fit recommendation or virtual try-on systems