Research on Personalized Recommendation Algorithm of Tourism E-commerce Platform Products Based on Data Mining

At present, information technology can be described as a rapid development, which brings about the development of tourism e-commerce platform in full swing and is welcomed by the public. The user demand is also increasing, and the trend of personalization is constantly showing, which makes the recommendation technology of the platform face opportunities and challenges. In order to further improve the service level of tourism e-commerce and meet the personalized tourism needs of users, based on the familiarity with data mining algorithms, this paper uses K-means clustering algorithm to build a personalized recommendation system for tourism e-commerce, and uses density method to optimize and improve the K-means clustering algorithm. Through the experiment in this paper, we can find that the algorithm improved in this paper has great advantages in applying to the personalized recommendation system of tourism e-commerce. On the one hand, it can truly meet the personalized needs of users and recommend them the personalized tourism products they need. On the other hand, it can effectively improve the purchase of tourism products and enhance the efficiency of enterprises, It has certain practical value to enhance the competitiveness of tourism e-commerce platform.