Research On Personalized Recommendation Of Tourist Routes Based On Improved Apriori Algorithm

With the continuous improvement of the application of computer technology in the tourist management system of agricultural parks,each scenic spot pays more attention to the analysis and processing of tourist information data,so many tourist data can be saved well.Mining potentially valuable information from these saved tourist data can not only help tourists play efficiently,but also assist the managers of scenic spots in decision-making.Moreover,many agricultural parks do not analyze and use the saved tourist data,and only do ordinary management work.Therefore,there is no way to make full use of the relevant and deep-seated potential laws hidden in the tourist information,resulting in a significant reduction in tourists' interest in playing experience.The purpose of this thesis is to improve the defects of the traditional Apriori algorithm in the personalized recommended tour route,construct the tourist diversion function,determine the starting point and end point of the diversion,and formulate the personalized recommended tour route and the recommended tour route in case of congestion.The purpose is to enhance the tourist experience in the scenic spot and enhance the accuracy of the scenic spot's recommended route.Firstly,the tourist information saved by the management platform of Qinhuangdao Jifa agricultural park is used as the experimental data source,and then the defects of the current scenic spot recommendation to tourists are analyzed from the research status at home and abroad.At the same time,it briefly introduces the location and climate conditions of Jifa agricultural park,and expounds the relevant theories of association rule technology in detail,which lays a good theoretical basis for the research direction of this thesis.Secondly,this thesis analyzes the shortcomings of the traditional data mining technology Apriori algorithm in the agricultural park management platform.In order to better explore the potential information hidden in the data such as age,children and gender in the tourist information,an improved Apriori algorithm based on hash table is proposed,and constraints are introduced into this algorithm to eliminate abnormal data,reduce the repeated scanning of the database and reduce the time complexity of the algorithm,So as to improve the personalized recommendation level of the park.Finally,through the improved Apriori algorithm,this thesis analyzes and studies the tourist information data of Qinhuangdao Jifa agricultural park,obtains the relevant laws of the age,whether to take children,gender and other factors in the tourist information on the selection of scenic spots,and finally designs the personalized recommended tour route.