@Article{2019.100000108,
AUTHOR = {Yanmei Zhang, Xiao Geng, Shuiguang Deng},
TITLE = {A Novel Service Recommendation Approach in Mashup Creation},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {3},
PAGES = {513--525},
URL = {http://www.techscience.com/iasc/v25n3/39682},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000108}
}