Trustworthy service recommendation has become indispensable for the success of the service ecosystem. However, traditional trustworthy methods somehow overlook the service equality which result into a “rich-get-richer” effect and become a barrier for the novice services to startup and grow. This paper addresses this problem through a novel equitable trustworthy mechanism, which distinguished the difference between the novice and mature services over the trustworthy service recommendation. The results based on the real-world service ecosystem, i.e. ProgrammableWeb, show that our method achieves a better performance in equality guarantee and white-washing prevention. Thus it can promote the service ecosystem’s healthy growth in a fair manner.
CITATION STYLE
Huang, K., Liu, Y., Nepal, S., Fan, Y., Chen, S., & Tan, W. (2014). A novel equitable trustworthy mechanism for service recommendation in the evolving service ecosystem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8831, pp. 510–517). Springer Verlag. https://doi.org/10.1007/978-3-662-45391-9_43
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