Learning service recommendations Online publication date: Thu, 31-Jul-2014
by Alexander Jungmann; Bernd Kleinjohann; Lisa Kleinjohann
International Journal of Business Process Integration and Management (IJBPIM), Vol. 6, No. 4, 2013
Abstract: The as a service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilised on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our approach of modelling this service composition and recommendation process as Markov decision process and of solving it by means of reinforcement learning. A case study serves as proof of concept.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Process Integration and Management (IJBPIM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook