Research On Tourism Product Recommendation System Based On Rough Set

With the development of society and improvement of people’s living standard, tourism has gradually become important part of human beings’ life. Generally speaking, tourism decision usually requires enough information. In other words, tourism is particularly dependent on information. In recent years, information technology has realized widespread application and tourism websites thus spring up quickly, providing users abundant traveling information and tourism products reservation service. Though spreading quantities of unfiltered information, those websites may also cover information users are interested in, bring inconvenience for their decision and travel planning. To meet individual meets with comprehensive service and filter redundant information, scholars transfer focus on intelligent tourism recommending system.This paper first makes deep research and analysis on tourism products and traveling decision, those could be helpful to understand the impact factors in people deciding to buy tourism products, and then constructed the tourism products ontology. On this basis, the paper described the approach of user information data acquisition, selected the user model expression method, the user model update method, and then built a comprehensive user model. Then the users of travel website are divided into registered and unregistered ones, content-based recommendation technology and hybrid recommendation techniques were used to establish the recommended model. Because the tourism products have much more attributes or concepts, the paper combined the rough set attribute reduction method to extract the key attributes to avoid the interference of redundant information and reduce subsequent computational complexity.The recommended model constructed in this paper can express user demand comprehensively and accurately, because of the representation of user model by Ontology. Finally, by experiments, the paper proved the tourism products recommended by the model constructed in this paper can meet the demand of consumers objectively.