A method of intelligent recommendation using task ontology

This study proposed a method of developing an intelligent recommendation system for automotive parts assembly. The proposed system will display the detailed information and the list components which make up the relevant part that an user wants through the database using the ontology when selecting an automotive part that an user intends to learn or to be guided of. This study is to design task ontology based on Hierarchical Taxonomy so as to achieve productivity enhancement, cost reduction and outcome improvement through recommendations based on intelligence and personalization depending on the worker’s present situation or context of task in charge when assembly of automotive parts is conducted. For this, composing elements of an engine and upper/lower relationships were expressed using hierarchical structure Taxonomy. The intelligent recommendation system for parts is offered to users through determining the automatic recommendation order between parts using the weights. This study has experimented the principles of the recommendation system and the method of setting the weights by setting two scenarios.