A recommender system is a system that helps users filter irrelevant information and create user interest models based on their historical records. With the continuous development of Internet information, recommendation systems have received widespread attention in the industry. In this era of ubiquitous data and information, how to obtain and analyze these data has become the research topic of many people. In view of this situation, this paper makes some brief overviews of machine learning-related recommendation systems. By analyzing some technologies and ideas used by machine learning in recommender systems, let more people understand what is Big data and what is machine learning. The most important point is to let everyone understand the profound impact of machine learning on our daily life.

翻译：推荐者系统是一个帮助用户过滤不相干信息并根据其历史记录创建用户兴趣模型的系统。随着互联网信息的持续发展,推荐系统在业界得到了广泛的关注。在这个数据和信息无处不在的时代,如何获取和分析这些数据已成为许多人的研究课题。鉴于这种情况,本文简要概述了与机器学习有关的建议系统。通过分析在推荐者系统中机器学习使用的一些技术和想法,让更多的人了解什么是大数据,什么是机器学习。最重要的一点是让所有人都了解机器学习对我们日常生活的深刻影响。