A personalized counseling system using case-based reasoning with neural symbolic feature weighting (CANSY)

In this article, we introduce a personalized counseling system based on context mining. As a technique for context mining, we have developed an algorithm called CANSY. It adopts trained neural networks for feature weighting and a value difference metric in order to measure distances between all poss...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2008-12, Vol.29 (3), p.279-288
Main Author: Ha, Sungho
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Classification
Computer Science
Intelligence
Machines
Manufacturing
Mechanical Engineering
Mining
Neural networks
Personalized
Processes
Reasoning
Studies
Weighting
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Summary:In this article, we introduce a personalized counseling system based on context mining. As a technique for context mining, we have developed an algorithm called CANSY. It adopts trained neural networks for feature weighting and a value difference metric in order to measure distances between all possible values of symbolic features. CANSY plays a core role in classifying and presenting most similar cases from a case base. Experimental results show that CANSY along with a rule base can provide personalized information with a relatively high level of accuracy, and it is capable of recommending appropriate products or services.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-007-0094-7