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Article

Sequential Recommendations on GitHub Repository

Department of Image Science and Arts, Chung-Ang University, Dongjak, Seoul 06974, Korea
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Author to whom correspondence should be addressed.
Academic Editors: Ángel González-Prieto and Fernando Ortega
Appl. Sci. 2021, 11(4), 1585; https://doi.org/10.3390/app11041585
Received: 14 December 2020 / Revised: 26 January 2021 / Accepted: 2 February 2021 / Published: 10 February 2021
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. View Full-Text
Keywords: dataset; deep neural network; implicit feedback; recommendation system; sequential recommendation systems dataset; deep neural network; implicit feedback; recommendation system; sequential recommendation systems
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MDPI and ACS Style

Kim, J.; Wi, J.; Kim, Y. Sequential Recommendations on GitHub Repository. Appl. Sci. 2021, 11, 1585. https://doi.org/10.3390/app11041585

AMA Style

Kim J, Wi J, Kim Y. Sequential Recommendations on GitHub Repository. Applied Sciences. 2021; 11(4):1585. https://doi.org/10.3390/app11041585

Chicago/Turabian Style

Kim, JaeWon, JeongA Wi, and YoungBin Kim. 2021. "Sequential Recommendations on GitHub Repository" Applied Sciences 11, no. 4: 1585. https://doi.org/10.3390/app11041585

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