Enhancing Recommendation Systems with Bayesian Probability: A Data-Driven Approach

This paper presents a data-driven exploration of enhancing recommendation systems through Bayesian probability. By leveraging Bayes' theorem and conditional probability, the study outlines how these mathematical concepts can improve personalization across digital platforms. From product suggestions on e-commerce sites to content curation on streaming and social media, the research demonstrates how real-time probability updates lead to smarter, more user-centric recommendations. The paper also offers practical examples and insights into how these approaches are currently used across industries, highlighting their relevance in today’s AI landscape.