Recommending Relevant Open Source Projects on GitHub using a Collaborative-Filtering Technique

摘要

The GitHub website represents nowadays an essential tool for developers from around the world; it is considered as a social network for them in which they can share their open source projects with others in a form of repositories. This paper presents and discusses the design and the implementation of a new recommender system for GitHub repositories based on a collaborative-filtering approach, which can be useful in many ways in the process of searching for the right solutions to build projects. The GitHub website is becoming very popular these days, a lot of projects are shared by millions of developers, building this recommender system can reduce searching time and make search results more and more relevant. The authors evaluate their system by conducting a set of experiments on a real data set using different well-known metrics and the k-fold cross validation method. Results obtained from these experiments are very promising, the authors found that their recommender system can reaches better precision and recall accuracy.