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

Brand recommendations for cold-start problems using brand embeddings

This paper presents our work to recommend brands to customers that might be relevant to their style but the brands are new to them. To promote the exploration and discovery of new brands, we leverage article-embeddings, also known as Fashion DNA, a learned en- coding for each article of clothing at Zalando, that is utilized for product and outfit recommendations. The model used in Fashion DNA’s work proposed a Logistic Matrix Factorization approach using sales data to learn customer style preferences. In this work, we evolved that approach to circumvent the cold-start problem for recommending new brands that do not have enough sales or digital footprint. First, we computed an embedding per brand, named Brand DNA, from the Fashion DNA of all articles that belong to a given brand. Then, we trained a model using Logistic Matrix Factorization to predict sales for a set of frequent customers and brands. That allowed us to learn customer style representations that can be leveraged to predict the likelihood of purchasing from a new brand by using its Brand DNA. Customers are also able to further explore Zalando’s assortment moving from the more popular products and brands

Deconstructing the right to privacy considering the impact of fashion recommender systems on an individual’s autonomy and identity

Computing ‘fashion’ into a system of algorithms that personalise an individual’s shopping journey is not without risks to the way we express, assess, and develop aspects of our identity. This study uses an interdisciplinary research approach to examine how an individual’s interaction with algorithms in the fashion domain shapes our understanding of an individual’s privacy, autonomy, and identity. Using fashion theory and psychology, I make two contributions to the meaning of privacy to protect notions of identity and autonomy, and develop a more nuanced perspective on this concept using ‘fashion identity’. One, a more varied outlook on privacy allows us to examine how algorithmic constructions impose inherent reductions on individual sense-making in developing and reinventing personal fashion choices. A “right to not be reduced” allows us to focus on the individual’s practice of identity and choice with regard to the algorithmic entities incorporating imperfect semblances on the personal and social aspects of fashion. Second, I submit that we need a new perspective on the right to privacy to address the risks of algorithmic personalisation systems in fashion. There are gaps in the law regarding capturing the impact of algorithmic personalisation systems on an individual’s inference of knowledge about fashion, as well as the associations of fashion applied to individual circumstances. Focusing on the case law of the European Court of Human Rights (ECtHR) and the General Data Protection Regulation (GDPR), as well as aspects of EU non-discrimination and consumer law, I underline that we need to develop a proactive approach to the right to privacy entailing the incorporation of new values. I define these values to include an individual’s perception and self-relationality, describing the impact of algorithmic personalisation systems on an individual’s inference of knowledge about fashion, as well as the associations of fashion applied to individual circumstances. The study concludes with recommendations regarding the use of AI techniques in fashion using an international human rights approach. I argue that the “right to not be reduced” requires new interpretative guidance informing international human rights standards, including Article 17 of the International Covenant on Civil and Political Rights (ICCPR). Moreover, I consider that the “right to not be reduced” requires us to consider novel choices that inform the design and deployment of algorithmic personalisation systems in fashion, considering the UN Guiding Principles on Business and Human Rights and the EU Commission’s Proposal for an AI Act