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Ontology - driven personalized expert recommender system for IT service management

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dc.contributor.advisor Silva ATP
dc.contributor.author Chamalka KSWKBL
dc.date.accessioned 2022
dc.date.available 2022
dc.date.issued 2022
dc.identifier.citation Chamalka, K.S.W.K.B.L. (2022). Ontology - driven personalized expert recommender system for IT service management [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21483
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21483
dc.description.abstract Finding experts related to a given query in an industrial environment is a timeconsuming manual task. Much research has been conducted in this area using multiple intelligent techniques, but still, there are research gaps with personalizing the recommendation accurately. In this context, an expert recommender system should consider the expert’s preference, experience, and other factors as well as complex organizational processes involved in the recommendation task. Also achieving high accuracy with other conflicting conditions simultaneously is a popular topic in recent research related to recommender systems. This thesis presents our hybrid approach to enhance the personalized expert recommendation problem in enterprise context. We integrate semantic-based ontology with the TOPSIS based Artificial Bee Colony algorithm to achieve high accuracy in this problem domain. Ontology is used for knowledge modeling of the expert profiles and the TOPSIS-ABC algorithm is used for ranking the profiles for a given query based on the distance to the ideal solution. en_US
dc.language.iso en en_US
dc.subject EXPERT RECOMMENDER SYSTEMS en_US
dc.subject MULTIOBJECTIVE OPTIMIZATION en_US
dc.subject ABC ALGORITHM en_US
dc.subject TOPSIS METHOD en_US
dc.subject ARTIFICIAL INTELLIGENCE - Dissertation en_US
dc.subject COMPUTATIONAL MATHEMATICS - Dissertation en_US
dc.title Ontology - driven personalized expert recommender system for IT service management en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty IT en_US
dc.identifier.degree MSc in Artificial Intelligence en_US
dc.identifier.department Department of Computational Mathematics en_US
dc.date.accept 2022
dc.identifier.accno TH5017 en_US


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