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Edge-cloud-enabled matrix factorization for diversified APIs recommendation in mashup creation.
- Source :
- World Wide Web; Sep2022, Vol. 25 Issue 5, p1809-1829, 21p
- Publication Year :
- 2022
-
Abstract
- A growing number of web APIs published on the Internet allows mashup developers to discover appropriate web APIs for polishing mashups. Developers often have to manually pick and choose several web APIs from extremely massive candidates, which is a laborious and cumbersome task. Fortunately, recommender system comes into existence. Some approaches perform recommendations in cloud platforms by utilizing historical records of Mashup-API interactions stored in edge nodes. However, many of these methods often pay more attention to recommendation accuracy while ignoring recommendation diversity, i.e., there are usually popular web APIs in recommendation list while most of the other novel web APIs are absent. The poor recommendation diversity may limit the usefulness of the recommendation results due to the lack of novelty. In order to implement an accurate and diversified web API recommendation, a novel MF-based recommendation approach named Div_Pre<subscript>API</subscript> is put forward in this paper. Div_Pre<subscript>API</subscript> integrates a weighting mechanism and neighborhood information into matrix factorization (MF) to implement diversified and personalized APIs recommendations. Finally, we conduct a series of experiments on a real-world dataset. Experimental results show the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]
- Subjects :
- MATRIX decomposition
RECOMMENDER systems
INTERNET publishing
CLOUD computing
Subjects
Details
- Language :
- English
- ISSN :
- 1386145X
- Volume :
- 25
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- World Wide Web
- Publication Type :
- Academic Journal
- Accession number :
- 159530857
- Full Text :
- https://doi.org/10.1007/s11280-021-00943-x