In our previous post, we showed you how to use image recognition to solve the issue of misattribution in e-commerce catalogs. Once you start to trust your models and have trained them to detect a valuable amount of attributes, it is easy to expand from attribute verification to auto tagging. However, our approach to misattribution is only very efficient when you already have a training set with a large set of images. The question remains, how can we take advantage of this technology when we only have a couple of examples in our library? In this post, we’ll show you how to solve this issue by building an image similarity function, which can be used to build image search and fill attribution gaps.
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Detecting and correcting e-commerce catalog misattribution with image and text classification using Google TensorFlow
In our previous post, we discussed the impact of product misattribution in e-commerce and how image recognition with Machine Learning can be an important tool to resolve this issue. In this post, we will get into the details of how to detect and correct misattribution using Machine Learning, Google TensorFlow and image vectorization.
A richly attributed and well-curated product catalog is the key asset of online retailers. However, products are frequently misattributed, which makes it a pain for customers to find the products they’re looking for.
For many years, chatbots have been quietly moving from the screens of sci-fi movies to commercial applications. Companies like Google, Amazon, Apple, IBM, and Microsoft, along with an ever-increasing array of AI startups, have been gradually perfecting Conversational User Interfaces (CUIs) that allow people to interact with computers in natural language. In 2011, Apple’s Siri become the first mass-market chatbot application to be widely distributed, as part of iOS. It captured the imagination of millions of consumers and showed that practical AI applications had a place in everyday life. Then — Wham! — in the holiday season of 2016, the Echo became Amazon’s best-selling consumer product, thus signaling that the era of CUIs had finally arrived in a big way.