GitHub project recommendation based on knowledge graph and developer similarity

Abstract Finding and recommending projects that match developer’s interests is always an urgent problem in open-source community. There are some problems in the existing project recommendation methods, such as insufficient use of information, ignoring the relationship between projects, one-sided consideration, and so on. To solve the above problems, we propose a project recommendation model based on project knowledge graph and developer similarity, called knowledge graphs and developer interest similarity (KGDS). KGDS mines developer interest from project similarity and developer similarity. For project similarity, we first construct the project knowledge graph. Then, content features and potential features are extracted from the project Readme document and knowledge graph, respectively, and the two features are merged to enrich the developer embedding and project embedding, which solves the problem of insufficient utilization of information. For developer similarity, we first construct a developer-project matrix, then obtain the historical developers related to candidate project, and then calculate the similarity between the historical developers and the target developer, which solves the problem of one-sided consideration. Finally, we combine the two part information to recommend projects that meet the interests of developers. We have conducted experiments on the GitHub dataset, and the results show that KGDS outperforms the baseline model.