Integrating Big Data and Blockchain with AI for Enhanced Movie Recommendation Systems: A Hybrid Approach Leveraging Text-toNumber Conversion and Cosine Similarity

Abstract

In the evolving landscape of digital content consumption, movie recommendation systems have
become integral in providing personalized experiences. This paper presents a novel hybrid
approach that integrates Big Data and Blockchain technologies with Artificial Intelligence (AI) to
enhance the performance and security of movie recommendation systems. The proposed model
leverages text-to-number conversion techniques combined with cosine similarity measures to
improve the accuracy of recommendations. Big Data analytics is employed to process and analyze
vast datasets of user preferences and movie attributes, while Blockchain ensures data integrity and
transparency. The synergy between these technologies not only enhances the recommendation
process but also addresses critical challenges such as data privacy and security. Experimental
results demonstrate the effectiveness of the proposed approach in delivering precise and secure
movie recommendations, paving the way for future advancements in AI-driven recommendation
systems.