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POYRAZ: CONTEXT‐AWARE SERVICE SELECTION UNDER DECEPTION
Author(s) -
Şensoy Murat,
Zhang Jie,
Yolum Pinar,
Cohen Robin
Publication year - 2009
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2009.00348.x
Subject(s) - service provider , service (business) , context (archaeology) , selection (genetic algorithm) , service design , computer science , deception , service level objective , service delivery framework , business service provider , consumer behaviour , marketing , business , psychology , social psychology , artificial intelligence , paleontology , biology
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.