Due to the rapid growth in both the number and diversity of web services on the Internet, it becomes increasingly difficult for developer to find the desired and appropriate web services for Mashup creation. Even if t...
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ISBN:
(纸本)9781728111414
Due to the rapid growth in both the number and diversity of web services on the Internet, it becomes increasingly difficult for developer to find the desired and appropriate web services for Mashup creation. Even if the existing approaches show improvements in webapis recommendation, it is still challenging to recommend webapis with high accuracy and good diversity. Some of them integrate functionality clustering and the quality of service to recommend webapis for Mashup creation, but do not consider the high-order composition interaction relationship among functionality information, quality attributes. In this paper, we propose a novel webapis recommendation method via integrating the functionality clustering of service and the quality of service. In this method, it firstly obtains the functionality clustering by using Doc2Vec to cluster the description document of webapis. Then, the deep factorization machine model is used to extract the multi-dimension quality attributes of service and mine the high-order composition interaction relationship between them. Finally, the comparative experiments are performed on Programmable web dataset and experimental results show that our method significantly improves the performance of web api recommendation in term of precision, recall, purity, entropy, DCG and HMD.
The rapid increase in the number and diversity of webapis with similar functionality, makes it challenging to find suitable ones for mashup development. In order to reduce the number of similarly functional webapis,...
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ISBN:
(数字)9783030235543
ISBN:
(纸本)9783030235543;9783030235536
The rapid increase in the number and diversity of webapis with similar functionality, makes it challenging to find suitable ones for mashup development. In order to reduce the number of similarly functional webapis, recommender systems are used. Various web api recommendation methods exist which attempt to improve recommendation accuracy, by mainly using some discovered relationships between webapis and mashups. Such methods are basically incapable of recommending quality webapis because they fail to incorporate webapi quality in their recommender systems. In this work, we propose a method that considers the quality features of webapis, to make quality web api recommendations. Our proposed method uses webapi quality to estimate their relevance for recommendation. Specifically, we propose a matrix factorization method, with quality feature regularization, to make quality web api recommendations and also enhance recommendation diversity. We demonstrate the effectiveness of our method by conducting experiments on a real-world dataset from ***. Our results not only show quality web api recommendations, but also, improved recommendation accuracy. In addition, our proposed method improves recommendation diversity by mitigating the negative Matthew effect of accumulated advantage, intrinsic to most existing webapi recommender systems. We also compare our method with some baseline recommendation methods for validation.
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