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A Novel Service Recommendation Approach in Mashup Creation

Yanmei Zhang1, Xiao Geng2, Shuiguang Deng3

1 Information School, Central University of Finance and Economics Beijing 100081, China
2 School of Computer Science, Wuhan University, Wuhan 430072 China
3 College of Computer Science and Technology Zhejiang University, Hangzhou 310027, China

* Corresponding Author: Yanmei Zhang, email

Intelligent Automation & Soft Computing 2019, 25(3), 513-525.


With the development of service computing technologies, the online services are massive and disordered now. How to find appropriate services quickly and build a more powerful composed service according to user interests has been a research focus in recent years. Current service recommendation algorithms often directly follow the traditional recommendation framework of ecommerce, which cannot effectively assist users to complete dynamic online business construction. Therefore, a novel service recommendation approach named UISCS (User-Interest- initial Services-Correlation-successor Services) is proposed, which is designed for interactive scenario of service composition, and it mines the user implicit interests and the service correlations for service recommendation. A series of experiments are conducted on a real-world dataset crawled from the ProgrammableWeb, and the results show that as a step-by-step service recommendation approach, the UISCS approach has obviously improved the performance of some mainstream recommendation algorithms, such as LDA, ICF , SVD and graph-based TSR.


Cite This Article

APA Style
Zhang, Y., Geng, X., Deng, S. (2019). A novel service recommendation approach in mashup creation. Intelligent Automation & Soft Computing, 25(3), 513-525.
Vancouver Style
Zhang Y, Geng X, Deng S. A novel service recommendation approach in mashup creation. Intell Automat Soft Comput . 2019;25(3):513-525
IEEE Style
Y. Zhang, X. Geng, and S. Deng "A Novel Service Recommendation Approach in Mashup Creation," Intell. Automat. Soft Comput. , vol. 25, no. 3, pp. 513-525. 2019.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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