Deep learning framework for multi‐round service bundle recommendation in iterative mashup development
Abstract Recent years have witnessed the rapid development of service‐oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service‐based systems such as mashups. Timely recommending appropriate component services for develop...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-09-01
|
| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12135 |
| _version_ | 1797682973485563904 |
|---|---|
| author | Yutao Ma Xiao Geng Jian Wang Keqing He Dionysis Athanasopoulos |
| author_facet | Yutao Ma Xiao Geng Jian Wang Keqing He Dionysis Athanasopoulos |
| author_sort | Yutao Ma |
| collection | DOAJ |
| description | Abstract Recent years have witnessed the rapid development of service‐oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service‐based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single‐round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively. |
| first_indexed | 2024-03-12T00:07:40Z |
| format | Article |
| id | doaj.art-f327af21ca594fcd9af8c167c7d5f3f4 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-03-12T00:07:40Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-f327af21ca594fcd9af8c167c7d5f3f42023-09-16T16:19:34ZengWileyCAAI Transactions on Intelligence Technology2468-23222023-09-018391493010.1049/cit2.12135Deep learning framework for multi‐round service bundle recommendation in iterative mashup developmentYutao Ma0Xiao Geng1Jian Wang2Keqing He3Dionysis Athanasopoulos4School of Computer Science Wuhan University Wuhan ChinaSchool of Computer Science Wuhan University Wuhan ChinaSchool of Computer Science Wuhan University Wuhan ChinaSchool of Computer Science Wuhan University Wuhan ChinaSchool of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast Belfast UKAbstract Recent years have witnessed the rapid development of service‐oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service‐based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single‐round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively.https://doi.org/10.1049/cit2.12135attentiondeep learningmashup developmentrecommender systemsservice bundle |
| spellingShingle | Yutao Ma Xiao Geng Jian Wang Keqing He Dionysis Athanasopoulos Deep learning framework for multi‐round service bundle recommendation in iterative mashup development CAAI Transactions on Intelligence Technology attention deep learning mashup development recommender systems service bundle |
| title | Deep learning framework for multi‐round service bundle recommendation in iterative mashup development |
| title_full | Deep learning framework for multi‐round service bundle recommendation in iterative mashup development |
| title_fullStr | Deep learning framework for multi‐round service bundle recommendation in iterative mashup development |
| title_full_unstemmed | Deep learning framework for multi‐round service bundle recommendation in iterative mashup development |
| title_short | Deep learning framework for multi‐round service bundle recommendation in iterative mashup development |
| title_sort | deep learning framework for multi round service bundle recommendation in iterative mashup development |
| topic | attention deep learning mashup development recommender systems service bundle |
| url | https://doi.org/10.1049/cit2.12135 |
| work_keys_str_mv | AT yutaoma deeplearningframeworkformultiroundservicebundlerecommendationiniterativemashupdevelopment AT xiaogeng deeplearningframeworkformultiroundservicebundlerecommendationiniterativemashupdevelopment AT jianwang deeplearningframeworkformultiroundservicebundlerecommendationiniterativemashupdevelopment AT keqinghe deeplearningframeworkformultiroundservicebundlerecommendationiniterativemashupdevelopment AT dionysisathanasopoulos deeplearningframeworkformultiroundservicebundlerecommendationiniterativemashupdevelopment |