Open Access
ARTICLE
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,
Intelligent Automation & Soft Computing 2019, 25(3), 513-525. https://doi.org/10.31209/2019.100000108
Abstract
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.
Keywords
Cite This Article
Y. Zhang, X. Geng and S. Deng, "A novel service recommendation approach in mashup creation,"
Intelligent Automation & Soft Computing, vol. 25, no.3, pp. 513–525, 2019. https://doi.org/10.31209/2019.100000108