Utilization of Deep Learning based feature extraction for Improved Recommendation System

Abstract

The world of global community groups, social media platforms, and business websites now offers a wealth of information on goods, people, and activities. This is resulting in an abundance of stuff that has to be handled effectively in order to get the needed information. According to the user’s preferred preferences, a recommendation system (RS) makes suggestions for pertinent goods to them. It handles a variety of user and item-related data. However, data sparsity is a problem for RSs. Deep examination of item contents is often done in RSs using deep learning approaches to provide exact suggestions. However, there is still need for future study into how to manage user reviews while concurrently doing item evaluations. In order to address the sparsity issue, a hybrid approach that simultaneously manages user and item information is put forward in this study.