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

Year: 2025

Type: article

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16157880

Author: Esheshwari Kumari, Esheshwari Kumari

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6950039635

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