Open source community developer recommendation method based on deep learning and unsupervised clustering

Abstract

The invention discloses an open source community developer recommendation method based on deep learning and unsupervised clustering. A deep learning neural network is combined with unsupervised clustering; the method is used for developer recommendation in an open source community. The method mainly comprises three steps, firstly, according to general feature information of developers, clusteringthe developers through unsupervised clustering; obtaining categories and ratios of different developers participating in each project; and then performing developer category prediction by using the project information and the developer category information based on the deep neural network, and finally performing training by using the deep neural network to obtain feature vectors corresponding to developers, thereby performing similarity comparison with different categories of developers to recommend corresponding developers. Good recommendation precision and efficiency can be obtained in a large-scale open source software community, the defects of existing research in the aspect of open source software community research can be overcome, and a new open source software developer recommendation method is provided for guaranteeing open source software development quality from a new perspective.

Description

Technical field [0001] The invention relates to a method for recommending developers to an open source software community by using deep learning and unsupervised learning methods, and belongs to the technical field of group intelligence software development. Background technique [0002] Mainstream recommendation algorithms are mainly divided into recommendation based on collaborative filtering, recommendation based on content, and mixed recommendation of the two. In collaborative filtering recommendation, the characteristic information data of users or items is not obtained, and users are often modeled based on the user's historical behavior data, so as to dig out the user's preference information, so as to recommend the items or items of interest to the user. According to the different mining methods, it is mainly divided into user-based collaborative filtering and item-based collaborative filtering. Although the collaborative filtering recommendation method has been applied i...

AI Technical Summary

Problems solved by technology

[0003] In terms of developer recommendation, few studies have used collaborative filtering-based recommendation and content-based recommendation for developer recommendation.

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