Abstract
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.
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An erratum to this article can be found at http://dx.doi.org/10.1007/s10489-007-0113-8
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Ha, S. A personalized counseling system using case-based reasoning with neural symbolic feature weighting (CANSY). Appl Intell 29, 279–288 (2008). https://doi.org/10.1007/s10489-007-0094-7
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DOI: https://doi.org/10.1007/s10489-007-0094-7