Composition pattern-aware web service recommendation based on depth factorisation machine
Web service composition has become a prevalent software development method that enables developing powerful Mashups by effectively combining Web services with different functions. However, as the number of Web services increases, it becomes challenging for developers to select appropriate services t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-10-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1911933 |
| _version_ | 1797684066360754176 |
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| author | Bing Tang Mingdong Tang Yanmin Xia Meng-Yen Hsieh |
| author_facet | Bing Tang Mingdong Tang Yanmin Xia Meng-Yen Hsieh |
| author_sort | Bing Tang |
| collection | DOAJ |
| description | Web service composition has become a prevalent software development method that enables developing powerful Mashups by effectively combining Web services with different functions. However, as the number of Web services increases, it becomes challenging for developers to select appropriate services to develop Web applications that satisfy functional requirements. In order to recommend Web services considering user's preferences, a composition pattern-aware Web service recommendation method called EWACP-DeepFM is proposed, which combines the composition patterns between Web services and Mashups and the co-occurrence and popularity of Web services. By constructing a multi-dimensional feature matrix, which is further trained by the depth factorisation machine (DeepFM) model to learn potential link relationships between Web services and Mashup applications, and recommend Top-N best services for the target Mashup application. Experiments performed using the real datasets from ProgrammableWeb show that the proposed method outperforms others with better recommendation effectiveness. |
| first_indexed | 2024-03-12T00:24:00Z |
| format | Article |
| id | doaj.art-52d9e8ca048947299c8fba85eac2470c |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:00Z |
| publishDate | 2021-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-52d9e8ca048947299c8fba85eac2470c2023-09-15T10:47:59ZengTaylor & Francis GroupConnection Science0954-00911360-04942021-10-0133487089010.1080/09540091.2021.19119331911933Composition pattern-aware web service recommendation based on depth factorisation machineBing Tang0Mingdong Tang1Yanmin Xia2Meng-Yen Hsieh3Hunan University of Science and TechnologyGuangdong University of Foreign StudiesHunan University of Science and TechnologyProvidence UniversityWeb service composition has become a prevalent software development method that enables developing powerful Mashups by effectively combining Web services with different functions. However, as the number of Web services increases, it becomes challenging for developers to select appropriate services to develop Web applications that satisfy functional requirements. In order to recommend Web services considering user's preferences, a composition pattern-aware Web service recommendation method called EWACP-DeepFM is proposed, which combines the composition patterns between Web services and Mashups and the co-occurrence and popularity of Web services. By constructing a multi-dimensional feature matrix, which is further trained by the depth factorisation machine (DeepFM) model to learn potential link relationships between Web services and Mashup applications, and recommend Top-N best services for the target Mashup application. Experiments performed using the real datasets from ProgrammableWeb show that the proposed method outperforms others with better recommendation effectiveness.http://dx.doi.org/10.1080/09540091.2021.1911933web service recommendationcomposition patternsdepth factorisation machinemashupweb api |
| spellingShingle | Bing Tang Mingdong Tang Yanmin Xia Meng-Yen Hsieh Composition pattern-aware web service recommendation based on depth factorisation machine Connection Science web service recommendation composition patterns depth factorisation machine mashup web api |
| title | Composition pattern-aware web service recommendation based on depth factorisation machine |
| title_full | Composition pattern-aware web service recommendation based on depth factorisation machine |
| title_fullStr | Composition pattern-aware web service recommendation based on depth factorisation machine |
| title_full_unstemmed | Composition pattern-aware web service recommendation based on depth factorisation machine |
| title_short | Composition pattern-aware web service recommendation based on depth factorisation machine |
| title_sort | composition pattern aware web service recommendation based on depth factorisation machine |
| topic | web service recommendation composition patterns depth factorisation machine mashup web api |
| url | http://dx.doi.org/10.1080/09540091.2021.1911933 |
| work_keys_str_mv | AT bingtang compositionpatternawarewebservicerecommendationbasedondepthfactorisationmachine AT mingdongtang compositionpatternawarewebservicerecommendationbasedondepthfactorisationmachine AT yanminxia compositionpatternawarewebservicerecommendationbasedondepthfactorisationmachine AT mengyenhsieh compositionpatternawarewebservicerecommendationbasedondepthfactorisationmachine |