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 a...

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Bibliographic Details
Published in:Cluster computing 2014-09, Vol.17 (3), p.827-833
Main Authors: Han, Jung-Soo, Kim, Gui-Jung
Format: Article
Language:English
Subjects:
Assembly
Automobile engines
Automotive engines
Automotive parts
Collaboration
Computer Communication Networks
Computer Science
Customer services
Data mining
Knowledge management
Ontology
Operating Systems
Piston rings
Processor Architectures
Product development
Recommender systems
Sensors
Shopping
Taxonomy
Water pipes
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Summary: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.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-013-0288-1