Ontology - driven personalized expert recommender system for IT service management

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.