Development of a Personalized Recommendation System with High Data Protection

This paper addresses the development of a personalized recommendation system aimed at improving user experience while ensuring a robust level of data confidentiality. The work describes in detail the critical stages of the recommendation methodology, including the creation of feature vectors representing specific object attributes, the structured collection and secure storage of user interaction datasets, and the client-side computation of personalized preference vectors. A recipe search application implementing the proposed approach is analyzed to demonstrate the efficacy and practical applicability of the developed recommendation algorithms. Particular attention is devoted to the detailed exploration and justification of the recommendation algorithms, data-processing techniques, and privacy preservation mechanisms incorporated within the proposed system. Furthermore, the study discusses the advantages of utilizing client-side computation to mitigate data disclosure risks, thereby facilitating enhanced user trust and compliance with privacy standards. In conclusion, the author outlines potential directions for further research and highlights the possibility for extending and optimizing the presented methodology across other application domains beyond recipe recommendations.