Thesis Abstract

Abstract
The rise of online shopping has revolutionized the retail industry, offering consumers convenience and a wide range of choices. With the increasing popularity of e-commerce platforms, retailers are constantly seeking ways to enhance the online shopping experience and improve customer satisfaction. One key aspect of online shopping is product recommendations, which play a crucial role in guiding consumers towards making purchasing decisions. This research project focuses on the development and implementation of a personalized recommendation system for an e-commerce platform to enhance the shopping experience for customers. The personalized recommendation system utilizes machine learning algorithms to analyze user behavior and preferences, generating tailored product recommendations based on individual shopping patterns. By incorporating collaborative filtering and content-based filtering techniques, the system can provide accurate and relevant product suggestions to users, ultimately increasing the likelihood of successful purchases. Additionally, the recommendation system is designed to adapt and improve over time as it gathers more data on user interactions and feedback. Through the implementation of the personalized recommendation system, retailers can benefit from increased sales and customer loyalty. By offering personalized product recommendations, retailers can create a more engaging and personalized shopping experience for customers, leading to higher levels of customer satisfaction and repeat business. Furthermore, the system can help retailers optimize their product assortment and promotional strategies by identifying trends and patterns in customer preferences. Overall, the personalized recommendation system presented in this research project has the potential to significantly enhance the online shopping experience for customers and drive business growth for retailers. By leveraging machine learning algorithms and data analytics, retailers can provide personalized product recommendations that cater to individual preferences and shopping behaviors. As e-commerce continues to evolve, implementing advanced recommendation systems will be essential for retailers to stay competitive and meet the changing needs of consumers in the digital marketplace.

Thesis Overview