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

摘要

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.