信息时代，互联网上的信息量巨大，数据信息给我们的生活带来许多便利的同时，也带来了信息超载问题。协同过滤算法应运而生，作为成功的个性化推荐技术，得到了广泛的应用。它分析用户的行为，通过收集与用户兴趣一致的其他用户的评价信息来产生推荐。然而，传统的推荐算法存在数据稀疏时相似度计算不准确，以及冷启动、可扩展性问题，影响了推荐系统的应用和推广。本文研究了协同过滤推荐技术的基本原理及实现步骤，提出了一种改进的相似度度量方法，可以在不进行复杂计算的情况下，通过提高数据的使用率来很好地提高推荐的准确性。
In the information age, there is a huge amount of information on the Internet. While data infor-mation brings a lot of convenience to our life, it also brings the problem of information overload. Collaborative filtering (CF) algorithm emerges as a successful personalized recommendation technique and is widely used. It analyzes the behavior of users and generates recommendations by collecting the evaluation information of other users who are in line with their interests. However, the traditional recommendation algorithm has some problems such as inaccurate similarity cal-culation when data is sparse, cold start and scalability, which affects the application and promotion of the recommendation system. In this paper, the basic principle and implementation steps of collaborative filtering recommendation technology are studied, and an improved similarity measurement method is proposed, which can improve the accuracy of prediction by improving the utilization rate of data without complex calculation.