An Intelligent Recommendation Model with a Case Study on u-Tour Taiwan of Historical Momuments and Cultural Heritage

Although there are many recommendation systems in use, they all face various challenges including the integration of diverse source data, improvement of prediction precision and meeting the user's satisfaction. In order to increase success rate of satisfied recommendations as well as the applications in different domain fields, we propose an Intelligent Recommendation Model and conduct a case study on the historical monuments and cultural heritage of u-Tour Taiwan to show the feasibility of our model. In this research we use a hybrid approach to combine effective techniques such as popularization-based, community filtering, demographic profiling, and expertise-based in accordance with the type of users and the amount of available data to adjust weight values. We also use association rules of data mining technique to find potential patterns in the web access log, while clustering is used to assign users into different groups suitable for them. The incremental approach of our method can calculate the ranking value of content to be more precise. Finally, Adobe Flex is used to present the recommendation result of Taiwan's 300 years of rich historical monuments and cultural heritage that provides more effective and efficient user interaction with less effort. Making full use of the valuable digital information of historical sites with our model, we hope to revitalize contemporary cultural and historical meaning that can bring people a brighter future and colorful life.