activity
20132019
most citedA Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

41 citations · 63 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2019

A Hierarchical Bayesian Model for Size Recommendation in Fashion

Romain Guigourès, Yuen King Ho, Evgenii Koriagin +3

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 cust…

cs.LG201941 cited

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

Abdul-Saboor Sheikh, Romain Guigoures, Evgenii Koriagin +4

Personalized size and fit recommendations bear crucial significance for any fashion e-commerce platform. Predicting the correct fit drives customer satisfaction and benefits the bu…

cs.CV2019

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

Nour Karessli, Romain Guigourès, Reza Shirvany

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 a…

cs.DB2015

Cats & Co: Categorical Time Series Coclustering

Dominique Gay, Romain Guigourès, Marc Boullé +1

We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid model…

cs.DB20152 cited

Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models

Romain Guigourès, Dominique Gay, Marc Boullé +2

Call Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network:…

cs.LG201320 cited

A Triclustering Approach for Time Evolving Graphs

Romain Guigourès, Marc Boullé, Fabrice Rossi

This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the sou…