Bayesian Models for Product Size Recommendations

Sembium, Vivek; Rastogi, Rajeev; Tekumalla, Lavanya Sita; Saroop, Atul

doi:10.1145/3178876.3186149

Cited by 30 publications

(28 citation statements)

References 26 publications

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“…With the aim of unifying the different sizes across size systems and brands, [9] propose a method to automate size normalization to a common latent space through the use of articles ordered data, generating mapping of any article size to a common space in which sizes can be better compared. More recently, there has been emerging research addressing the problem of personalized size recommendation for online fashion retailers [1,7,11,13,20,21,[26][27][28]. Given the order history of a customer (or personal customer data such as age, weight, height, etc.…”

Section: Related Workmentioning

confidence: 99%

“…To that end, we present a Bayesian model which uses size-related return data from customers to learn which articles would fit normally and which would exhibit a size issue. Unlike previous work [1,7,11,13,20,21,[26][27][28], our approach extensively leverages the size-related return rates of articles to greatly focus on modeling articles sizing behaviour. Although the (weakly annotated and subjective) return data from customers is leveraged in the model, the latter is agnostic to the specific customer who places the order and thus by design does not provide a customer with a personalized size recommendation; instead it informs them about article specific sizing characteristics.…”

Section: Related Workmentioning

confidence: 99%

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SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce

Nestler¹,

Karessli²,

Hajjar

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce

Nestler¹,

Karessli²,

Hajjar

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

show abstract

“…It is worth noting that methods described in [8,10] aim at modeling p(R | S, C, A). Doing so requires discretizing the continuous variable S leading to an increase in the number of parameters to be inferred and making the model more susceptible to the sparsity of the data.…”

Section: Hierarchical Bayesian Modelmentioning

confidence: 99%

“…To handle multiple persons behind a single account, hierarchical clustering is performed on each customer account before doing the above. An extension of that work has been very recently published proposing a Bayesian approach on a similar model [10]. Instead of learning the parameters in an iterative process, the updates are done with mean-field variational inference with Polya-Gamma augmentation.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical bayesian model for size recommendation in fashion

Guigourès¹,

Ho²,

Koriagin³

et al. 2018

Proceedings of the 12th ACM Conference on Recommender Systems

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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.

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“…Most of the published work on size recommendations and fit prediction is quite recent. Sembium et al [3] proposed a latent factor model and a follow-up Bayesian formulation [4] to predict a simple fit prediction (small, fit, large) given a size of a product for a customer. Other work recently proposed a hierarchical Bayesian model [5] for personalized size recommendation.…”

Section: Introductionmentioning

confidence: 99%

Meta-Learning for Size and Fit Recommendation in Fashion

Lasserre¹,

Sheikh²,

Koriagin³

et al. 2020

Proceedings of the 2020 SIAM International Conference on Data Mining

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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 retraining. 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.

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Bayesian Models for Product Size Recommendations

Cited by 30 publications

References 26 publications

SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce

SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce

A hierarchical bayesian model for size recommendation in fashion

Meta-Learning for Size and Fit Recommendation in Fashion

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