Name
Abstract | ||
|---|---|---|
•First in-depth investigation on the popularity of mashups using a real-world dataset.•Five factors were identified as key factors behind service mashup’s popularity.•Propose a Bayesian model that offers valuable design-phase predictions and insights.•Suggested approach can overcome data sparsity and capture popularity contribution.•Conduct extensive experiments over a ProgrammableWeb dataset (5 years period). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.eswa.2020.113231 | Expert Systems with Applications |
Keywords | DocType | Volume |
Popularity prediction,Bayesian learning,Service mashup | Journal | 149 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moayad Alshangiti | 1 | 0 | 0.34 |
| Weishi Shi | 2 | 6 | 4.23 |
| Xumin Liu | 3 | 471 | 34.87 |
| Qi Yu | 4 | 770 | 55.65 |