Research On Personalized Recommendation Technology Of Scenic Spots Based On Data Mining

As the science and technology and economic grow faster and faster,people's living standards are also improved,and more and more people are willing to relax themselves through tourism.The rapid development of science and technology has brought people a wealth of tourism resources and more travel options.At the same time,it also brings time consumption and energy consumption to people's choice of preferred travel information from these rich tourism resources.And in the process of tourism,users pay more attention to choosing tourism resources according to their own wishes,and hope that scenic spots can provide more personalized services.However,most of the traditional tourism industry neglects the psychological needs of users,thus making consumers stand in a passive position.To sum up,traditional tourism methods can no longer meet the different individual needs of different users.The development of personalized travel services for users by mining and analyzing historical travel data of users has become the best solution to solve the existing problems.At the same time,with the increase in the number of Weibo users,more and more data has been checked in based on the Weibo location check-in function.Through mining and analyzing the user's micro-blog check-in data in the scenic spot,the user's interest preference and the hot spot area of the scenic spot can be obtained,thereby providing the user with a personalized recommendation service for the scenic spot.This paper makes use of the micro-blog check-in POI data of the users in the scenic spot from January 1,2014 to October 31,2014 in Hubei Province to carry out the personalized recommendation research of the scenic spot.Through the K-Means clustering algorithm,the POI data of the user's scenic area microblog is clustered and analyzed to obtain the hot spot area.Then,according to the POI data in each cluster obtained by the K-Means clustering algorithm,the POI score of the scenic spot is calculated according to the popularity of the scenic spot POI and the user's preference,and the Top-N recommendation algorithm is used to generate the top scored scenic spot.Lastly,the POI recommendation list is recommended to users.Based on this,the personalized recommendation results of the scenic spot is got by integrating K-Means clustering algorithm and Top-N recommendation algorithm in this paper.In order to verify the quality of the algorithm recommendation results,this paper uses the form of a questionnaire to verify.The user selects the scenic spot POI data in each cluster based on his interest preferences,and the results of the user's selection are compared with the recommendation results generated by K-Means clustering algorithm and Top-N recommendation algorithm.And the recommendation results are verified from Precision and Recall respectively.The verification proves that the scenic spot personalized recommendation results based on K-Means clustering algorithm and Top-N recommendation algorithm proposed in this paper has a good recommendation effect.