Deep learning based web service recommendation methods: A survey

被引:2
作者
Mecheri, Karima [1 ]
Klai, Sihem [2 ]
Souici-Meslati, Labiba [1 ]
机构
[1] Badji Mokhtar Annaba Univ, Dept Comp Sci, LISCO Lab, POB 12, Annaba, Algeria
[2] Badji Mokhtar Annaba Univ, Dept Comp Sci, LABGED Lab, Annaba, Algeria
关键词
Deep learning; recommendation systems; web services; mashup; quality of service; performance evaluation metrics; NEURAL-NETWORK; AWARE; FACTORIZATION; PREDICTION; QUALITY;
D O I
10.3233/JIFS-224565
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Web service recommender systems have a fundamental role in the selection, composition and substitution of services. Indeed, they are used in several application areas such as Web APIs and Cloud Computing. Likewise, Deep Learning techniques have brought undeniable advantages and solutions to the challenges faced by recommendations in all areas. Unfortunately, the field of Web services has not yet benefited well from these deep methods, moreover, the works using these methods for Web services domain are very recent compared to the works of other fields. Thus, the objective of this paper is to study and analyze state-of-the-art work on Web services recommender systems based on Deep Learning techniques. This analysis will help readers wishing to work in this field, and allows us to direct our future work concerning the Web services recommendation by exploiting the advantages of Deep Learning techniques.
引用
收藏
页码:9879 / 9899
页数:21
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