AI-Driven Personalization in Price Comparison Platforms: Balancing Efficiency and Privacy

Peter Tobi Sunmola

International Journal of Business and Management, 2026, vol. 21, issue 1, 15

Abstract: The rapid expansion of e-commerce has made Price Comparison Platforms (PCPs) essential tools for consumers seeking efficient and informed purchasing decisions. AI-driven personalization improves platform efficiency by tailoring recommendations, interfaces, and offers to individual users while also enhancing engagement and conversion rates. However, personalization depends on the collection and processing of user data, raising privacy concerns such as data breaches, re-identification risks, algorithmic bias, and potential erosion of trust. This paper proposes a conceptual framework integrating Data, Algorithm, and Interface & Policy layers to balance personalization benefits with privacy protection. Using an integrative literature review approach, the study synthesizes insights on personalization efficiency, privacy risks, and ethical governance. The analysis highlights how privacy-preserving AI techniques, user-centric controls, and fairness-aware algorithms can help maintain both platform performance and user trust. The paper concludes with practical recommendations for developers and policymakers. It identifies directions for future research, including explainable AI, context-aware federated learning, synthetic data generation, and longitudinal evaluation of user trust.

Date: 2026
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