Existing developer recommendation algorithms extract explicit features of tasks and developers by mining the explicit information of tasks and developers, so as to recommend developers to specific tasks. However, since the description information in the explicit information is subjective and often imprecise, the performance of existing developer recommendation algorithms based on explicit features is not ideal. The crowdsourcing software development platforms not only have a lot of imprecise description information, but also contain objective and more accurate “task-developer” score information, which can effectively infer implicit features of tasks and developers. Considering that implicit features are supplements to explicit features, which will effectively alleviate the problem of imprecise description information, this study proposes a developer hybrid recommendation algorithm that combines explicit features and implicit features. First, the explicit features are fully extracted from the visible information of tasks and the developers on the platform, and the explicit features-oriented factorization machine (FM) recommendation model is proposed to learn the relationship between explicit features of tasks and developers and the corresponding ratings. Then, implicit features are inferred with the "task-developer" rating matrix, and the implicit features-oriented matrix factorization (MF) recommendation model is proposed. Finally, a multi-layer perceptron fusion algorithm is proposed to fuse the explicit features-oriented FM recommendation model and implicit features-oriented MF recommendation model. Further, for the cold-start problem, first, based on historical data, a multi-layer perceptron model is utilized to learn the mapping relationship between explicit features and implicit features. Then, for the cold-start tasks or the cold-start developers, the implicit features are obtained through their explicit features. Finally, the ratings are predicted based on the trained multi-layer perceptron fusion algorithm. The simulation experiment on the Topcoder software crowdsourcing platform shows that the proposed algorithm outperforms the comparison algorithms significantly in terms of four different evaluation metrics.