Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem. A large number of researchers have published hundreds of papers to improve different RS features. It is advisable to use RS frameworks that simplify RS researchers: a) to design and implement recommendations methods and, b) to speed up the execution time of the experiments. In this paper, we present CF4J, a Java library designed to carry out Collaborative Filtering based RS research experiments. CF4J has been designed from researchers to researchers. It allows: a) RS datasets reading, b) full and easy access to data and intermediate or final results, c) to extend their main functionalities, d) to concurrently execute the implemented methods, and e) to provide a thorough evaluation for the implementations by quality measures. In summary, CF4J serves as a library specifically designed for the research trial and error process.

翻译：推荐系统（RS）是缓解信息过载问题的有效工具。大量研究人员已发表数百篇论文以改进推荐系统的不同特性。使用推荐系统框架有助于研究人员完成以下工作：a）设计与实现推荐方法，b）加速实验执行时间。本文提出CF4J——一个专为基于协同过滤的推荐系统研究实验而设计的Java库。CF4J由研究人员为研究人员设计，支持：a）读取推荐系统数据集，b）完整且便捷地访问数据、中间及最终结果，c）扩展其主要功能，d）并发执行已实现方法，e）通过质量指标对实现方案进行深入评估。总之，CF4J作为专为研究试错过程设计的库，为科研工作提供了有力支撑。