Optimization Strategy for Personalized Recommendation System Based on Data Analysis

Abstract

Personalized recommendation systems utilize comprehensive user information analysis to deliver customized content and services across various domains, including e-commerce, online media, and social networking. These systems have significantly improved user engagement and experience by aligning recommendations with individual preferences and behaviors. However, their practical implementation still faces challenges such as data imbalance, algorithmic homogenization, and concerns over privacy and security. This study explores the essential role of data analysis in enhancing personalized recommendation mechanisms, emphasizing its applications in data mining, pattern recognition, and real-time data processing. To address existing limitations, several optimization strategies are proposed, including the integration of multi-source heterogeneous data, the adoption of hybrid recommendation models, the implementation of robust privacy-preserving technologies, and the incorporation of user feedback mechanisms for continuous correction. By adopting these optimization approaches, recommendation systems can achieve greater accuracy, robustness, and adaptability. The enhanced systems not only improve the precision and personalization of recommendations but also contribute to higher user satisfaction and platform profitability, providing a more intelligent and secure user experience.