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Open Source Software (OSS) is forming the spines of technology infrastructures, attracting millions of talents to contribute. Notably, it is challenging and critical to consider both the developers’ interests and the semantic features of the project code to recommend appropriate development tasks to OSS developers. In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects. We introduce CODER, a novel graph-based CODE Recommendation framework for open source software developers, which accounts for the complex interactions among multiple parties within the system. CODER jointly models microscopic user-code interactions and macroscopic user-project interactions via a heterogeneous graph and further bridges the two levels of information through aggregation on filestructure graphs that reflect the project hierarchy. Moreover, to overcome the lack of reliable benchmarks, we construct three largescale datasets to facilitate future research in this direction. Extensive experiments show that our CODER framework achieves superior performance under various experimental settings, including intraproject, cross-project, and cold-start recommendation.