Artificial Intelligence Review - Special issue on lazy learning
Web usage mining for Web site evaluation
Communications of the ACM
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Internet Marketing: Integrating Online and Offline Strategies
Internet Marketing: Integrating Online and Offline Strategies
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Personalized Course Navigation Based on Grey Relational Analysis
Applied Intelligence
Personalization technologies: a process-oriented perspective
Communications of the ACM - The digital society
Context-based Preference Analysis Method in Ubiquitous Commerce
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
A Framework for Automatic Online Personalization
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature-based recommendations for one-to-one marketing
Expert Systems with Applications: An International Journal
Hybrid system of case-based reasoning and neural network for symbolic features
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Context-aware semantic discovery for next generation mobile systems
IEEE Communications Magazine
A hybrid approach of neural network and memory-based learning to data mining
IEEE Transactions on Neural Networks
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Introducing attribute risk for retrieval in case-based reasoning
Knowledge-Based Systems
A case-based knowledge system for safety evaluation decision making of thermal power plants
Knowledge-Based Systems
Engineering Applications of Artificial Intelligence
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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.