Inproceedings,
Personalization and Recommendation Systems: Leveraging Machine Learning Algorithms to Offer Personalized Product Recommendations and Content to Customers Based on Their Behavior, Preferences and Purchasing History
Personalization and Recommendation Systems: Leveraging Machine Learning Algorithms to Offer Personalized Product Recommendations and Content to Customers Based on Their Behavior, Preferences and Purchasing History, 16, page 4. (June 2025)
DOI: 10.5121/ijgca.2025.16201
Abstract
Traditional approaches have failed to keep up with privacy requirements as AI and ML systems increasingly interact with vast, sensitive data sets. Currently, most solutions treat privacy as strictly a technical problem or legal checkbox, but rarely both. The presented work this paper uses a multidimensional scheme that serves to reframe data privacy as a fundamental design principle through the integration of four key perspectives: technical structure, regulatory fit, organizational preparedness, and ethical responsibility. We assess such privacy preserving solutions, federated learning, secure multiparty computation, differential privacy, among others, against the real-world constraints such as scalability, latency and compliance. With cross-sector case study and comparative analysis we illustrate that hybrid context-aware deployments can bridge the theory-practice gap. Our contribution is more than just a toolkit: it is a systems-level implementation that allows businesses and organizations to operationalize privacy while embracing innovation. Such work is necessary for future empirical studies to provide a grounded approach to the development of AI systems that are high performing, yet privacy preserving.
