Research on the Application of Machine Learning-based Artificial Intelligence Algorithms in Recommendation Systems

Abstract

Recommendation systems are one of the important applications in the field of artificial intelligence, and machine learning-based artificial intelligence algorithms play a key role in recommendation systems. This paper conducts an in-depth exploration of the application of machine learning-based artificial intelligence algorithms in recommendation systems. The main contents include the definition and importance of recommendation systems, the basic principles and methods of machine learning, the potential and applications of artificial intelligence in recommendation systems, the working principles of recommendation systems, common types of recommendation systems, as well as application strategies from aspects such as data collection and preprocessing, feature engineering, model selection and training, evaluation and optimization, user feedback and iterative improvement, etc. The aim is to explore the potential and application strategies of machine learning algorithms in recommendation systems. Through comprehensive analysis and summary, it is found that machine learning-based artificial intelligence algorithms have great potential in recommendation systems, enabling personalized, accurate, and real-time recommendation results, thereby improving user satisfaction and recommendation effectiveness. In the future, the focus and challenges of further research will include privacy and data security issues, real-time recommendation and personalized strategies, as well as user feedback and iterative improvement.

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