2 publications

• Published Subscription Original Research 10 Apr 2026

  A Privacy-Preserving and Performance-Optimized Machine Learning Framework for Personalized E-Commerce User Experience

  By Ravi Bhushan Kumar, Manish Sharma

  Abstract: In contemporary e-commerce websites, merchant suggestions are the most outstanding way to boost customer satisfaction and conversion levels. Nevertheless, the growing dependence on user data provokes significant concerns over the protection of privacy and efficiency in computation. In this paper, we have suggested a privacy-preserving and performance-optimized machine learning framework that allows customized e-commerce experiences and protects the sensitive information of users. The framework incorporates the concepts of federated learning …

  Published in E-Commerce for Future & Trends · Vol. 13, Issue 2, 2026 · pp. 1–8 Read article →

• Published Subscription Original Research 5 Apr 2026

  Adaptive and Explainable ML Framework for Personalized E-Commerce User Experience and Satisfaction

  By Ravi Bhushan Kumar, Manish Sharma

  Abstract: The user experience and user satisfaction have taken the center of the stage as a determinant of platform success in the fast-changing e-commerce ecosystem. Conventional recommendation systems are usually run in black-box models that give minimal transparency and dynamicity to altered user preferences. The study will introduce a personalization framework of explainable machine learning (XML) based on explainable artificial intelligence (XAI) and hybrid recommendation strategies. The model uses Collaborative Filtering …

  Published in Current Trends in Information Technology · Vol. 16, Issue 2, 2025 · pp. 23–32 Read article →