Abstract

This systematic review aims to identify the changes in the field of personalization in consumer digital products. The study includes research from 2023 to 2026. The study includes 15 research papers and provides a collection of the latest developments in recommendation systems, privacy-preserving personalization, personalization explanations, federated learning, and adaptive interfaces. The study provides a review of how personalization is changing to privacy-preserving personalization. The study highlights federated learning as a promising direction in achieving personalization without compromising user data. The study highlights the effectiveness of graph neural networks and transformers in recommendation systems, achieving 12% or higher accuracy than traditional collaborative filtering. However, personalization is still a major challenge. The study highlights the need to achieve a balance between deep personalization and privacy, as well as adapting to change in real-time and across platforms. The study will be helpful to anyone who wants to learn about personalization techniques.

References

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