Towards a Novel and Intelligent e-commerce Framework for Smart-Shopping Applications
Abstract
Nowadays, with the advancement of market digitalization accompanied by internet technologies, consumers can buy products from anywhere in the world. Finding the best-offered deal from numerous e-commerce sites and online stores is overwhelming, time-consuming, and often not very effective. Customers need to visit many online stores to find their desired product at the desired price. Also, the option of finding a product in the future time that is not currently available is limited in the current e-commerce platform. To address these limitations, there is a need to develop a new one-stop e-shopping model that would allow customers to search for any product in the marketplace from one central application. This thesis proposes a novel and intelligent e-shopping application framework that takes specific product/s input from the users and searches the e-commerce sites to find the best deal. Our proposed framework is also capable of learning from customers’ complex behavior and their shopping experience, offering product recommendations accordingly. Our developed SmartCart prototype is a novel, effective, intelligent, scalable, and one-stop solution for shoppers to find desired products in real-time. Our proposed novel solution addresses some of the significant limitations of today's e-shopping platforms and is expected to enhance today’s digitally savvy e-shopping consumers.