GitHub is a software development platform that facilitates collaboration and participation in project development. Typically, developers search for relevant projects in order to reuse functions and identify useful features. Recommending suitable projects for developers can save their time. However, finding suitable projects among many projects on GitHub is difficult. In addition, different users may have different requirements. A recommendation system would help developers by reducing the time required to find suitable projects. In this paper, we propose an approach to recommend projects that considers developer behaviors and project features. The proposed approach automatically recommends the top-N most relevant software projects. We also integrate user feedback to improve recommendation accuracy. The results of an empirical study using data crawled from GitHub demonstrate that the proposed approach can efficiently recommend relevant software projects with relatively high precision.