ANALYSIS OF EXISTING METHODS AND TOOLS FOR DATA PROTECTION IN RECOMMENDER SYSTEMS

Javlonbek Ruzimov

Авторы

Javlonbek Ruzimov Urgench Branch of Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi

Ключевые слова:

Recommender Systems, Data Protection, Privacy, Security, Algorithmic Bias, GDPR, Personalization, User Profiling, Shilling Attacks, Data Governance

Аннотация

In the digital era, recommender systems play a vital role in shaping user experiences across various domains, including e-commerce, streaming services, and social media. However, these systems present significant data protection challenges, particularly in terms of user privacy, security, and algorithmic bias. This paper explores critical issues related to data collection, user profiling, and the ethical implications of personalization, highlighting concerns such as shilling attacks, sensitive data exposure, and compliance with global regulations like GDPR. The study further discusses legal frameworks, ethical considerations, and potential solutions, emphasizing the need for privacy-preserving techniques and regulatory compliance to ensure transparency and user trust in recommender systems.

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