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 |