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Collaboration efficiency is of primary importance in software development. It is widely recognized that choosing suitable developers is an efficient and effective practice for improving the efficiency of software development and collaboration. Recommending suitable developers is complex and time-consuming due to the difficulty of learning developers’ expertise and willingness. Existing works focus on learning developers’ expertise and interactions from their explicit historical information and matching them to specific task. However, such procedures may suffer low accuracy because they ignore implicit information, such as (1) developer–developer collaboration relationships, (2) developer–task implicit interaction relationships, and (3) task–task association relationships, etc. To that end, this paper proposes a multi-relationship fused approach for software developer recommendation (termed SoftRec). First, in addition to explicit developer–task interactions, it considers multivariate implicit relationships, including the three types mentioned above. Second, it integrates these relationships based on joint matrix factorization and generates forecast results upon the architecture of deep neural network. Furthermore, we propose a fast update method to address the cold start issue by making online recommendations for new developers and new tasks. Extensive experiments are conducted on two real-world datasets, and a user study is conducted in a well-known software company. The results demonstrate that SoftRec outperforms four state-of-the-art works.