Name
Abstract | ||
|---|---|---|
The number of APIs on the Web has increased rapidly in recent years. It becomes quite popular for developers to combine different APIs to build innovative Mashup applications. However, it is challenging to discover the appropriate ones from enormous APIs for Mashup developers (i.e., API users). In order to recommend a set of APIs that most satisfy the users' requirements, we propose a multi-relation based manifold ranking approach. The approach exploits the textual descriptions of existing Mashups and APIs, as well as their composition relationships. It firstly groups Mashups into different clusters according to their textual descriptions, then explores multiple relations between Mashup clusters and between APIs. Finally, it employs a manifold ranking algorithm to recommend appropriate APIs to the user. Experiments on a real-world dataset crawled from ProgrammableWeb.com validate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-49178-3_2 | ADVANCES IN SERVICES COMPUTING |
Keywords | Field | DocType |
Mashup clustering,Web API,Multi-relation,Manifold ranking,API recommendation | Web API,Mashup,Ranking SVM,Information retrieval,Computer science,Algorithm,Exploit,Manifold ranking | Conference |
Volume | ISSN | Citations |
10065 | 0302-9743 | 1 |
PageRank | References | Authors |
0.34 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fenfang Xie | 1 | 9 | 1.52 |
| Jianxun Liu | 2 | 640 | 67.12 |
| Tang Mingdong | 3 | 557 | 39.35 |
| Dong Zhou | 4 | 342 | 25.99 |
| Buqing Cao | 5 | 200 | 23.96 |
| Min Shi | 6 | 35 | 3.53 |