Visual Shop

Abstract

With over two decades of internet boom, shopping trends have changed a lot. People use human computer interaction for shopping online. After a decade of internet rise, intelligent systems were designed to interact with humans to assist in online shopping. These bots were trained to start conversations the way humans talk and helped searching products via text inputs or voice messages. These intelligent systems were not good at searching products via images or based on certain features. Mostly it is a hectic process for customers to describe these types of features while hunting for products online. This brings us to the need of searching by image assistant or Visual Shop which recommends products based on the image provided by the user. Users can capture a snap from the device directly or take any pictures from social media to follow the latest trends and search for the same products on an e-commerce store. A recommendation system helps in online shopping based on the previous shopping or search history. It uses stats and knowledge retrieval methods to come up with best matching product suggestions. It will be wired to provide relative outcomes to the customer queries by applying machine learning based image detection methods.