Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks
Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks Precise item categorization is a key issue in e-commerce domains. However, it still remains a challenging problem due to data size, category skewness, and noisy metadata. Here, we demonstrate a successful report on a deep learning-based item categorization method, i.e., deep categorization network (DeepCN), in a
Predicting Next-Season Designs on High Fashion Runway
Fashion is a large and fast-changing industry. Foreseeing the upcoming fashion trends is beneficial for fashion designers, consumers, and retailers. However, fashion trends are often perceived as unpredictable due to the enormous amount of factors involved into designers' subjectivity. In this paper, we propose a fashion trend prediction framework and design neural network models to leverage struc
Google Vizier: A Service for Black-Box Optimization
Daniel Golovin (Google, Inc.);Benjamin Solnik (Google, Inc.);Subhodeep Moitra (Google, Inc.);Greg Kochanski (Google, Inc.);John Karro (Google, Inc.);D. Sculley (Google, Inc.) Abstract Any sufficiently complex system acts as a black box when it becomes easier to experiment with than to understand. Hence, black-box optimization has become increasingly important as systems have become more complex. I
Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to R
Home / Topics Images Don’t Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank Corey Lynch, ; Kamelia Aryafar*, Etsy Inc.; Josh Attenberg, Etsy Abstract Search is at the heart of modern e-commerce. As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem. Traditional models optimize ove
Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks
Large-Scale Item Categorization in e-Commerce Using Multiple Recurrent Neural Networks Precise item categorization is a key issue in e-commerce domains. However, it still remains a challenging problem due to data size, category skewness, and noisy metadata. Here, we demonstrate a successful report on a deep learning-based item categorization method, i.e., deep categorization network (DeepCN), in a
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