With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Due to the flexibility and openness, a large number of external contributors are attracted to the open source communities. They are playing a significant role in open source development. However, the online open source development is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks to work on from thousands of open source projects in the communities. In this paper, a new approach, called RepoLike, is proposed for recommending repositories to developers based on linear combination and learning to rank. It utilizes the project popularity, technical dependencies among projects and social connections among developers to measure the correlations between a developer and the given projects. The experiment results show that this new approach can achieve over 25% of hit ratio when recommending 20 candidates, which means it can recommend closely correlated repositories to social developers.