Poyraz: Context-Aware Service Selection under Deception

Murat Sensoy, Jie Zhang, Pinar Yolum, Robin Cohen

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


The increasing number of service providers on the Web makes it challenging to select a provider for a specific service demand. Each service consumer has different expectations for a given service in different contexts, so the selection process should be consumer-oriented and context-dependent. Current approaches for service selection typically have consumers receive ratings of providers from other consumers, where the ratings reflect the consumers' overall subjective opinions. This may be misleading if consumers have different contexts and satisfaction criteria. In this paper, we propose that consumers objectively record their experiences, using an ontology to capture subtle details. This can then be interpreted by consumers according to their own criteria and contexts. We then integrate a method for addressing consumers who lie about their experiences, filtering them out during service selection. We demonstrate the value of our approach through experiments comparing our model with three recent rating-based service selection models. Our experiments show that using the proposed approach, service consumers can select the service providers for their needs more accurately even if the consumers have different criteria, they change the contexts of their service demands over time, or a significant portion of them are liars.
Original languageEnglish
Pages (from-to)335-366
Number of pages32
JournalComputational Intelligence
Issue number4
Publication statusPublished - Nov 2009


  • multiagent systems
  • semantic web


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