Research And System Implementation Of Tourist Attraction Recommendation Model Based On Location Social Network

In today’s society,with the continuous development and progress of science and technology,positioning technology is becoming increasingly mature and various types of intelligent mobile devices are widely used.Location-based social network(Location-based Social Network,LBSN)has become a very important part of life,work and study.Among the services related to location social network,location recommendation service has been paid more and more attention and promoted,and point of interest(Point of Interest,POI)recommendation technology comes into being.After obtaining the historical check-in data of users,the point of interest recommendation technology made an in-depth analysis of the obtained data,explored the users’ interests and preferences,and provided a recommendation list according to their preferences.This thesis mainly studied the model and system of tourist attraction recommendation based on location social network.Firstly,the check-in behavior of users was analyzed.On this basis,the existing scenic spot recommendation model was deeply analyzed.In view of the existing data sparse problems and cold start problems,a tourist spot recommendation model based on location social network was proposed,and the corresponding system was developed.This thesis mainly completed the following two aspects of work:(1)A tourist attraction recommendation model based on location social network has been proposed.This model integrated the influencing factors of social relations,the influencing factors of users with similar spatial distribution and the geographical influencing factors of tourist attractions,introduced second-level friends into the collaborative filtering algorithm,alleviated the problem of sparse data,and obtained the influence of the influencing factors of social relations on recommendation.Considering the geographical influencing factors of tourist attractions,the low accuracy of the existing collaborative filtering recommendation model was improved,and the discovery of user influence was introduced.Based on the acquisition of user influence,the geographical influence of tourist attractions was discovered,and the geographical influence of tourist attractions was deeply explored.At the same time,the influence factors of users with similar spatial distribution were considered,and the influence of users with similar spatial distribution was found on the basis of the calculation of spatial distribution similarity.Finally,considering the influence of the three factors,the probability of users visiting a tourist spot under the influence of these factors was calculated,and the recommendation list was constructed.Through the comparison experiment with different models,the experimental results showed that the location social network based tourist attraction recommendation model proposed in this thesis has significantly improved the accuracy rate,recall rate and F1 value evaluation index,and the recommendation effect of tourist attractions has been significantly improved.(2)A tourist attraction recommendation system based on location social network has been developed.This thesis took the above recommendation model as the core and completes the design and implementation of the tourist attraction recommendation system based on location social network.On the basis of the research,the demand analysis of the tourist attraction recommendation system,to determine the function of the system,including login registration,personal information management,tourist attraction personalized recommendation,tourist attraction popularity recommendation,tourism sharing,industry conditions,policies and regulations,scenic spot search,scenic spot evaluation and other functions.After realizing the recommendation model proposed in this thesis,the system analysis and design were carried out,and the tourist attraction recommendation system based on location social network was developed.On the one hand,the developed scenic spot recommendation system could well meet the expectations of tourists;on the other hand,it could also improve the user stickiness of the tourism platform,which has a good application prospect.