Research And Implementation Of AR Content Recommendation System In Smart Scenic Spot

In the context of the country actively promoting the high-quality development of smart tourism,new generation information technologies such as cloud computing,machine learning and 5G are deeply integrated with tourist attractions to promote the digital development of smart tourism.At the same time,new technologies such as Augmented Reality(AR),human-computer interaction,and machine learning have also brought more exciting travel experiences.However,the massive AR data often makes it difficult for tourists to choose because of the lack of personalized recommendation and real-time features.Therefore,in order to improve the efficiency and experience,so that the tour guide and recommendation service of the scenic spot can serve tourists and users more accurately and conveniently,this thesis proposes to build an AR-oriented content recommendation system in smart scenic spots.This thesis is oriented to the scene of smart scenic spots,and firstly studies the content recommendation process and technical mechanism based on AR.According to the feedback information of many scenic spots and combined with the needs of tourists,the recommendation algorithm and development platform are determined.Secondly,this thesis completes the design and implementation of the content recommendation algorithm.The recommendation algorithm adopts one-hot and multi-hot coding methods,and the age of tourists is grouped by K-means algorithm,so as to process the characteristic information of users and scenic spots.After the feature information processing is completed,the data without interaction between the processed features is fed into the deep interest evolution network model optimized based on the basic model.Due to the introduction of the attention mechanism,the currently recommended products can be correlated with the historical behavior of tourists,thereby improving the accuracy of the model.Through the analysis and verification of data experiments,it is proved that the proposed algorithm has good and stable data fitting,indicating that it can achieve content recommendation tasks in more complex scenarios.Based on the We Chat applet platform,this thesis designs and implements a featurerich and practical AR content recommendation system for smart scenic spots.This system mainly realizes the AR registration identification and tracking process,and completes the demand analysis,design and development of functions such as system maintenance management,AR content recommendation,and AR smart navigation.Judging from the test results of the “AR Content Recommendation System for Smart Scenic Areas”,the system can complete the smart navigation service based on AR,the system runs smoothly,and the interface is easy to operate,which can make the scenic area tour work more convenient,informatized,and effectively improved.It can improve the tourist experience and the intelligent management level of the scenic spot.