E-commerce platform based on Machine Learning Recommendation System
Muhammad Tahir, Rabia Noor Enam, Syed Muhammad Nabeel Mustafa
Abstract
Information overload is one of the potential setbacks to many e-commerce platform users. It is very important to filter the media and the choices that are overwhelming for internet users while making buying decisions using online stores. To solve this problem, recommendation systems are used widely. A recommender system helps users find a product of their own choice by filtering and prioritizing and effectively generating the relevant information to its users. The purpose of a recommender system is to save time and hassle of searching through the World Wide Web, instead it generates specific and relevant content that promotes online transaction and bring satisfaction to the users of e-commerce platforms. The proposed system is an e-commerce platform based on an apparel recommendation system that recommends products on the foundation of the user's preferences.