A Novel Recommender System for CyberSecurity-Dependent Business Processes

Abstract

As cloud computing continues to gain popularity, users are faced with an overwhelming number of cloud service options to choose from. The abundance of choices can make it challenging for users to identify the most appropriate cloud services for their needs. A cloud services recommender system can address this challenge by providing personalized recommendations based on user preferences and needs. This thesis proposes a novel cloud services recommender system that leverages user feedback to provide accurate and relevant recommendations. The system was evaluated through experiments on real-world cloud service datasets, demonstrating its effectiveness in improving the user experience and facilitating cloud service adoption. The findings indicated that the system we suggested outperformed existing recommender systems regarding recommendation accuracy and user satisfaction. The system can benefit both users and cloud service providers by simplifying the process of finding and selecting cloud services, increasing customer satisfaction, and promoting cloud service adoption. Future research directions include expanding the system to support new types of cloud services and incorporating additional user feedback sources. The contributions of this thesis include advancing the field of cloud computing and providing a practical solution to a significant challenge faced by cloud service users.