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Service recommendation using machine learning methods based on measured consumer experiences within a service market
Linnaeus University, Faculty of Technology, Department of Computer Science. Karlsruhe University of Applied Sciences, Germany.
Karlsruhe University of Applied Sciences, Germany.
Linnaeus University, Faculty of Technology, Department of Computer Science. (Software Technology Labs)ORCID iD: 0000-0002-7565-3714
2015 (English)In: International Journal on Advances in Intelligent Systems, ISSN 1942-2679, E-ISSN 1942-2679, Vol. 8, no 3&4, 347-373 p.Article in journal (Refereed) Published
Abstract [en]

Among functionally similar services, service consumersare interested in the consumption of the service that performsbest towards their optimization preferences. The experiencedperformance of a service at consumer side is expressed in its nonfunctionalproperties. Selecting the best-fit service is an individualaspect as the preferences of consumers vary. Furthermore, servicemarkets such as the Internet are characterized by perpetualchange and complexity. The complex collaboration of system environmentsand networks result in various performance experiencesat consumer side. Service optimization based on a collaborativeknowledge base of previous experiences of other, similar consumerswith similar preferences is a desirable foundation. In thisarticle, we present a service recommendation framework, whichaims at the optimization at consumer side focusing on the individualpreferences and call contexts. In order to identify relevantnon-functional properties for service selection, we conducted aliterature study of conference papers of the last decade. Theranked results of this study represent what a broad scientificcommunity determined to be relevant non-functional propertiesfor service selection. We furthermore analyzed, implemented, andvalidated machine learning methods that can be employed forservice recommendation. Within our validation, we could achieveup to 95% of the overall achievable performance (utility) gainwith a machine learning method that is focused on conceptdrift, which in turn, tackles the change characteristic of theInternet being a service market. Besides the comprehensive andscientific identification of relevant non-functional properties whenselecting a service, this article describes how machine learningcan be employed for service recommendation based on consumerexperiences in general, including an evaluation and overall proofof concept validation within our framework.

Place, publisher, year, edition, pages
2015. Vol. 8, no 3&4, 347-373 p.
National Category
Computer Science
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-50723OAI: oai:DiVA.org:lnu-50723DiVA: diva2:912031
Available from: 2016-03-15 Created: 2016-03-15 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
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Output format
  • html
  • text
  • asciidoc
  • rtf