Name
Abstract | ||
|---|---|---|
In open source communities, developers always need to spend plenty of time and energy on discovering specific projects from massive open source projects. Consequently, the study of personalized project recommendation for developers has important theoretical and practical significance. However, existing recommendation approaches have clear limitations, such as ignoring developers’ operating behavio... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TETC.2018.2870734 | IEEE Transactions on Emerging Topics in Computing |
Keywords | DocType | Volume |
Open source,project recommendation,deep auto-encoder,GitHub | Journal | 9 |
Issue | ISSN | Citations |
2 | 2168-6750 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengcheng Zhang | 1 | 24 | 8.52 |
| Fang Xiong | 2 | 0 | 0.68 |
| Hareton Leung | 3 | 0 | 0.34 |
| Wei Song | 4 | 2 | 1.37 |