Abstract

Abstract: In Today’s generation, it has become a need to get clear vision, prioritize and effortlessly to provide appropriate information to decrease the problem of information overload, which has created a major problem for many users using internet. Recommender systems is capable to solve this major problem by using searching technique through a huge volume of dynamically generated useful data to provide users with personalized content and services depending on their interest. Recommendation system plays an important role in the internet world and is used in many applications. Recommendation systems are of three types: 1) collaborative filtering 2) content based and 3) hybrid based approach. With the advancement of machine learning for decades, there are still numerous problems insolvable, such as image recognition and location detection, image classification, image generation, speech recognition, natural language processing and so on. In this emerging field of deep learning, the research on topic like image classification has always been the topmost traditional research to be evolved. Simultaneously, Image recognition technology is also beneficial to gradually respond in better way to the development of international indicators, and betterment the development and progress in various fields. Hence, image processing technology which is based on machine learning has always been widely used in feature image segmentation, classification and recognition, and is an important topic in various fields. It allows you to find similar products in your store in a very easy way. Let the customer find the product based on the photo. What's more, you no longer have to manually match similar products, it will be done automatically by AI algorithm. The main purpose of this project is to use the concept of a machine learning algorithm to build a Recommendation and Reverse Image Search and recognition with the help of convolutional neural networks (CNN).

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