I. Introduction
Artificial intelligence is a new and challenging subject. Since the birth of artificial intelligence has developed rapidly, giving rise to many branches. Such as reinforcement learning, simulation environment, intelligent hardware, machine learning, etc. However, the rapid development of artificial intelligence technology has brought a lot of convenience to people's lives[1]. As one of the important applications in the field of artificial intelligence, recommendation systems aim to provide users with personalized, accurate, and valuable recommendations. With the development of the internet and e-commerce, recommendation systems play a crucial role in various domains such as e-commerce, social media, music, and videos. The emergence of machine learning-based artificial intelligence algorithms has brought new opportunities and challenges to recommendation systems. Machine learning algorithms have the capability to mine users' interests and preferences from vast amounts of data, thereby offering personalized and accurate recommendations. However, in practical applications, effectively utilizing machine learning algorithms to construct efficient recommendation systems remains a complex and crucial problem. Therefore, through research and analysis, our objective is to provide best practices and future development recommendations regarding machine learning algorithms in recommendation systems. This aims to facilitate further improvements and innovations in recommendation systems.