Title: Learning service recommendations

Authors: Alexander Jungmann; Bernd Kleinjohann; Lisa Kleinjohann

Addresses: Cooperative Computing and Communication Laboratory, University of Paderborn, Fuerstenallee 11, 33102 Paderborn, Germany ' Cooperative Computing and Communication Laboratory, University of Paderborn, Fuerstenallee 11, 33102 Paderborn, Germany ' Cooperative Computing and Communication Laboratory, University of Paderborn, Fuerstenallee 11, 33102 Paderborn, Germany

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.

Keywords: service-oriented computing; service composition; service selection; service recommendation; reinforcement learning; sequential decision making; Markov decision process; MDP; on-the-fly computing; modelling; services.

DOI: 10.1504/IJBPIM.2013.059135

International Journal of Business Process Integration and Management, 2013 Vol.6 No.4, pp.284 - 297

Available online: 04 Feb 2014 *

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