A Personalized Recommendation Model for Tourism Products

Electronic Commerce has becomes an important means for tourism enterprises to face the increasing competition. How to provide the personalized service for customers is an important issue to raise the service level of tourism. Through analyzing characteristics of tourism, a Personalized Recommendation Model is proposed at the basis of user’s rating feature. It has following features: (1) Pre-processing user’s rating data to solve different rating criterion of different user. (2) According to user’s rating feature, a correction coefficient is set to ameliorate similarity among users, to improve the computational accuracy of the nearest neighbor. At last, experiments ware designed. Comparing with general collaborative filtering, the proposed algorithm has higher quality of recommendation.