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Appropriate machine learning methods for service recommendation based on measured consumer experiences within a service market
Linnaeus University, Faculty of Technology, Department of Computer Science. Karlsruhe University of Applied Sciences, Germany.
University of Applied Science Karlsruhe, Germany.
University of Applied Science Karlsruhe, Germany.
Linnaeus University, Faculty of Technology, Department of Computer Science. (Software Technology Labs)ORCID iD: 0000-0002-7565-3714
2015 (English)In: SERVICE COMPUTATION 2015 : The Seventh International Conferences on Advanced Service Computing, March 22, 2015 to March 27, 2015, Nice, France, International Academy, Research and Industry Association (IARIA), 2015, 41-48 p.Conference paper, Published paper (Refereed)
Abstract [en]

The actual experience of the performance of services at consumers’ side is a desirable foundation for service selection. Considering the knowledge of previous performance experiences from a consumer’s perspective, a service broker can automatically select the best-fitting service out of a set of functionally similar services. In this paper, we present the evaluation of machine learning methods and frameworks which can be employed for service recommendation based on shared experiences of previous consumers. Implemented in a prototype, our approach considers a consumer’s call context as well as its selection preferences (expressed in utility functions). The implementation of the framework aims at the time-critical optimisation of service consumption with focus on runtime aspects and scalability. Therefore, we evaluated and employed high-performance, online and large scale machine learning methods and frameworks. Considering the Internet as a service market with perpetual change, strategies for concept drift have to be found. The evaluation showed that with the current approach, the framework recommended the actual best-fit service instance in 70% of the validation cases, while in 90% of the cases, the best or second best-fit was recommended. Furthermore, within our approach employing the best method, we achieved 94.5% of the overall maximum achievable utility value.

Place, publisher, year, edition, pages
International Academy, Research and Industry Association (IARIA), 2015. 41-48 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-42472ISBN: 978-1-61208-387-2 (print)OAI: oai:DiVA.org:lnu-42472DiVA: diva2:805589
Conference
SERVICE COMPUTATION 2015 : The Seventh International Conferences on Advanced Service Computing, March 22, 2015 to March 27, 2015, Nice, France
Available from: 2015-04-15 Created: 2015-04-15 Last updated: 2017-01-27Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf