A STRATEGY TO ADVOCATE CLOUD MANUFACTURING SERVICE BASED ON THE IMAGINARY CLUSTERING AND IMPROVED SLOPE ONE ALGORITHM

Abstract

The booming growth of cloud
manufacturing services provides users with more
choices. However, cloud manufacturing service
recommendation remains a challenging issue due to
numerous similar candidate services and diverse user
preferences. The purpose of this paper is to provide
an efficient and accurate cloud manufacturing
service recommendation method. A spectral
clustering algorithm is first designed to cluster the
cloud manufacturing services.
Then the candidate rating service set is constructed
based on the service clusters by service function
comparison and parameter matching. Finally, an
improved Slope one algorithm, which integrates user
similarity and service similarity, is proposed to rate
the cloud manufacturing services. The top-k services
with the highest scores are recommended to the
users. Experiments show that the proposed method
can provide more accurate service rating with less
time consumption. The service recommendation
performance of this method is also proved to be
superior to other methods in terms of precision,
recall, and F-score.