Name
Abstract | ||
|---|---|---|
We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence information of user-repository interactions for the first time in the software service recommendation field. Specifically, a deep-learning based sequential recommendation technique is adopted to capture the dynamics of user preferences. Comprehensive experiments have been conducted on a large dataset collected from GitHub against a list of existing methods. The results illustrate the superiority of our method in various aspects. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-91431-8_45 | ICSOC |
DocType | ISSN | Citations |
Conference | ICSOC 2021 (2021) 691-699 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingwei Zhang | 1 | 10 | 2.52 |
| Jiayuan Liu | 2 | 0 | 1.01 |
| Weipu Zhang | 3 | 0 | 0.34 |
| Ke Deng | 4 | 0 | 2.37 |
| Hai Dong | 5 | 439 | 41.61 |
| Ying Liu | 6 | 30 | 32.81 |