A tutorial on case based reasoning
Soft computing in case based reasoning
Expert Systems with Applications: An International Journal
Predicting financial activity with evolutionary fuzzy case-based reasoning
Expert Systems with Applications: An International Journal
Fuzzy case-based reasoning for facial expression recognition
Fuzzy Sets and Systems
Building an expert travel agent as a software agent
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid multi-criteria decision-making model for firms competence evaluation
Expert Systems with Applications: An International Journal
Turist@: Agent-based personalised recommendation of tourist activities
Expert Systems with Applications: An International Journal
Improving user experience with case-based reasoning systems using text mining and Web 2.0
Expert Systems with Applications: An International Journal
A hybrid MCDM methodology for ERP selection problem with interacting criteria
Decision Support Systems
Expert Systems with Applications: An International Journal
Web intelligence for tourism using railway data by a simplified fuzzy reasoning method
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
Hi-index | 12.06 |
The accelerating interaction between technology and tourism has changed radically the efficiency and effectiveness of tourism organizations, as well as how consumers interact with organizations. In this study, a Web based intelligent framework for travel agencies is proposed that offers customers a fast and reliable response service in a less costly manner. The proposed framework integrates case-based reasoning (CBR) system with a well-known multi criteria decision making (MCDM) technique, namely Analytic Hierarchy Process, to enhance the accuracy and speed in case matching in tourism destination planning. The integration of two techniques enables taking advantages of their strengths and complements each other's weaknesses. A case study is performed to demonstrate how this framework can facilitate intelligent decision support by retrieving best-fitted responses for customers.