Recommender system suggests users with options that may be of use to them or may be of their interest or liking. These days recommender systems are used widely on most systems and especially on those which are connected to World Wide Web, it may be a mobile app, a desktop application, or a website. Most advertisements on these systems are focused on targeting a specific group. Recommender systems provide a solution to such a scenario where the recommendations need to be targeted based on a user profile. Almost all commercial, collaborative or even social networking websites rely on recommender systems. In this paper, we specifically focus on GitHub, a source code hosting site and one of the most popular platforms for online collaborative coding and sharing. GitHub offers an opportunity for researchers to perform analysis by providing REST-based APIs for downloading its data. GitHub hosts a vast amount of user repositories so it is quite difficult for a GitHub user to decide to which repository she should contribute on GitHub. So, our paper aims to review different approaches that can be used for creating a recommender system for GitHub, to provide personalized suggestions to GitHub users to which repositories they should contribute. In this paper, we have discussed collaborative filtering, content-based filtering, and hybrid filtering, knowledge-based and utility-based approaches of a recommender system.