| dc.description.abstract | Abstract: With the development of the world
wide web (WWW), the number of people
who can deal with their work through the
Internet, is increasing and it helps to do their
tasks effectively and efficiently. In this case, a
very important task is fulfilled by Web
services. But the main problem is users
struggling to select their favourite Web
services quickly and accurately among
available Web services. Web service
recommendations help to solve this problem
successfully. In this paper, we used
collaborative filtering (CF)-based
recommendation technique, but it suffers
from the data sparsity and cold-start
problem. Therefore, we applied an ontologybased
clustering approach to overcome these
problems. It effectively increased the data
density by assuming the missing user
preferences comparing the history of user
favoured domains. Then, user ratings are
predicted based on the model-based
approach such as singular value
decomposition (SVD). The result showed that
the clustering approach can overcome the CF
problems effectively and the SVD method can
predict user ratings with lower prediction
error compared with existing approaches. | en_US |
