Abstract
The software development platform is an increasingly expanding industry. It is growing steadily due to the active research and sharing of artificial intelligence and deep learning. Further, predicting users’ propensity in this huge community and recommending a new repository is beneficial for researchers and users. Despite this, only a few researches have been done on the recommendation system of such platforms. In this study, we propose a method to model extensive user data of an online community with a deep learning-based recommendation system. This study shows that a new repository can be effectively recommended based on the accumulated big data from the user. Moreover, this study is the first study of the sequential recommendation system that provides a new dataset of a software development platform, which is as large as the prevailing datasets. The experiments show that the proposed dataset can be practiced in various recommendation tasks.
Highlights
GitHub is one of the biggest software development platforms, wherein a large number of users upload their open-source projects
As a result of the augmentative computation-intensive nature of modern scientific discovery, this trend is largely growing in deep learning (DL) and machine learning fields; a clear example can be found where links are often provided to GitHub in published research papers
We evaluated our dataset by implementing gated recurrent units for recommendation (GRU4Rec) [4], convolutional sequence embedding recommendation model (Caser), and self-attentive sequential recommendation (SASRec) methods, which are DNN-based sequential recommendation algorithms
Summary
GitHub is one of the biggest software development platforms, wherein a large number of users upload their open-source projects. It stores a vast number of repositories of source codes related to researchers’ projects or research papers for them to share with more people online. To find useful information or repositories in GitHub, one needs to inspect projects manually using a search filter or search for a popular project on the Explore GitHub page (https://github.com/explore). GitHub provides such recommendations based on general recommendation topics, it depends heavily on temporally close and content-based relationships (category, language, etc.) as far as we have experienced
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