Over the past two decades, recommender systems have attracted a lot of interest due to the explosion in the amount of data in online applications. A particular attention has been paid to collaborative filtering, which is the most widely used in applications that involve information recommendations. Collaborative filtering (CF) uses the known preference of a group of users to make predictions and recommendations about the unknown preferences of other users (recommendations are made based on the past behavior of users). First introduced in the 1990s, a wide variety of increasingly successful models have been proposed. Due to the success of machine learning techniques in many areas, there has been a growing emphasis on the application of such algorithms in recommendation systems. In this article, we present an overview of the CF approaches for recommender systems, their two main categories, and their evaluation metrics. We focus on the application of classical Machine Learning algorithms to CF recommender systems by presenting their evolution from their first use-cases to advanced Machine Learning models. We attempt to provide a comprehensive and comparative overview of CF systems (with python implementations) that can serve as a guideline for research and practice in this area.

翻译：在过去二十年中,由于在线应用中的数据数量激增,建议系统引起了很大的兴趣; 特别注意协作过滤,这是在涉及信息建议的应用中最广泛使用的方法; 协作过滤(CF)利用一组用户已知的偏好,对其他用户的未知偏好作出预测和建议(根据用户过去的行为提出建议); 最初在1990年代引入了各种日益成功的模型; 由于机器学习技术在许多领域的成功,越来越强调在建议系统中应用这种算法; 在本条中,我们概述了CF方法用于建议系统、其两个主要类别及其评价指标; 我们侧重于将经典机器学习算法应用于CF建议系统,介绍其从第一个使用案例到先进机器学习模型的演变情况; 我们试图对CF系统(连同Python实施)进行全面和比较的概览,作为该领域研究和实践的指导方针。