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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
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
We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website. In this work we compare traditional machine learning techniques with the most advanced deep learning approaches. We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a substantial improvement by extracting features from high dimensional data during t
Fashwell automatically recognizes products in images and our vision is to make every image instantly shoppable. We achieve this by powering SaaS product recognition tools – like AI-powered search by image and recommendation engines – for fashion and furniture brands, retailers and eCommerce players across the globe. Easy mobile search and shopping based on images, not text. Like placing an image i
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