Master of Science
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.
Summary for Lay Audience
Today’s digital world has changed the way people shop. The current marketplace consists of online and offline stores offering various services to attract buyers. An average shopper now has access to numerous sellers and is exposed to a wide range of deals on identical products through multiple platforms. Consumers are forced to visit these individual platforms to find the best deal on their chosen product and thus spend considerable time on shopping research. This highlighted the need for a single e-commerce platform that consolidates existing deals and presents them to the end-user through a central e-shopping application. Everyday shopping also does not end at finding the availability and best price for one single product. This research identified the need for a complete shopping solution and proposed an intelligent solution to address that challenge. The thesis presents an innovative smart shopping framework that identifies the stores according to the user preferences, provides a centralized product lookup, and finds the stores that offer the desired products. The user is notified whenever a previously unavailable product becomes available. This novel model considers user preferences while locating the products and gives back real-time information about these products in a few seconds. Users’ shopping behaviour and experience are also monitored as part of our developed prototype, and our recommendation system can offer relevant products based on users’ shopping trend and preferences.
Hanumanthu, Susmitha, "Towards a Novel and Intelligent e-commerce Framework for Smart-Shopping Applications" (2022). Electronic Thesis and Dissertation Repository. 8818.
Available for download on Saturday, March 30, 2024