Web API recommendation via combining graph attention representation and deep factorization machines quality prediction

SUMMARY As more and more companies and organizations encapsulate and publish their business data or resources to the Internet in the form of APIs, the number of web APIs has grown exponentially. For this reason, it has become challenging to quickly and effectively find web APIs from such a large‐sca...

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Veröffentlicht in:Concurrency and computation Jg. 34; H. 21
Hauptverfasser: Cao, Buqing, Peng, Mi, Qing, Yueying, Liu, Jianxun, Kang, Guosheng, Li, Bing, Fletcher, Kenneth K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 25.09.2022
Wiley Subscription Services, Inc
Schlagworte:
Accuracy
Annotations
Application programming interface
Classification
DeepFM
Graphical representations
mashup application
Nodes
Recommender systems
self‐attention mechanism
service recommendation
ISSN:1532-0626, 1532-0634
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Zusammenfassung:SUMMARY As more and more companies and organizations encapsulate and publish their business data or resources to the Internet in the form of APIs, the number of web APIs has grown exponentially. For this reason, it has become challenging to quickly and effectively find web APIs from such a large‐scale web API collection, which meet the requirements of mashup developers. To this end, this article focuses on recommending suitable web APIs to build high‐quality mashups by classifying and integrating content‐oriented service functionality with service invocation prediction. The proposed web API recommendation method for mashup development uses graph attention representation and DeepFM quality prediction. First, it uses the web API composition and shared annotation relationships to construct a web API relationship network. Second, it applies the self‐attention mechanism to compute the attention coefficients of different neighboring nodes in the web API relationship network. So, for a specific web API node, the weighted sum of the importance of its neighboring nodes and features characterizes that web API node. Doing so ensures that the service can be divided more accurately into different functional clusters via high‐quality characterization. Third, for the web APIs in a cluster, the high‐quality representation results are combined with multidimensional quality of service attributes. It employs the DeepFM to model and mine complex interaction relationships between features and subsequently predict and rank the invocation scores of web APIs. Finally, experiments are compared and analyzed on real‐world web API datasets. It can be seen from the results of several groups of comparative experiments that the proposed method outperforms other nine baseline methods on accuracy, recall, F1, DCG, and AUC and achieved a good classification accuracy and recommendation effect.
Bibliographie:Funding information
Hunan Provincial Natural Science Foundation, National Natural Science Foundation of China, Grant/Award Numbers: 61873316; 61872139; 61832014; 61702181
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.7069