AR Learn+

With the rapid progress and development of the times,the way of learning is gradually developing towards lifelong,fragmented and mobile.Informal learning has gradually developed into an important way for people to acquire knowledge.In this context,people have also focused on how to obtain quality and interesting learning resources.Mobile augmented reality technology can create an informal learning environment that can be learned at any time or anywhere.It brings opportunities for providing high-quality learning resources.However,the existing mobile augmented reality applications are often single.At the same time,it also lacks diversified and exclusive interactive experience and personalized resources.Therefore,it often causes the problems of low frequency and short duration of use,which affects users'immersion and learning passion.Therefore,this thesis focuses on how to optimize the interactive experience of mobile augmented reality applications and the recommendation of augmented reality resources to improve the effect of informal learning.Therefore,this thesis mainly studies the design and implementation of "AR Learn+"-a popular science knowledge recommendation system.First of all,this thesis studies the user experience of mobile augmented reality applications and the current situation of existing technologies.On this basis,I analyzed the system requirements,completed the system function design and database design.Secondly,on the basis of model specification CELTS-11,I build a user model of popular science knowledge recommendation system.Thirdly,I study the classification of users' mental models in the augmented reality environment,and develop the content of augmented reality interaction design according to the results;at the same time,I use the improved recommendation algorithm-CBPR-TF to recommend the users' augmented reality popular science knowledge resources.The model integrates context information,such as AR resource feature preference,geographic location preference and so on.The results show that it improves the accuracy of Resource Recommendation in augmented reality.Finally,this thesis builds a "AR Learn+" popular science knowledge recommendation system.I use unity3d and Android studio as the client development environment.I use Eclipse as the server development environment.The system realizes the functions of scanning,resource recommendation,popular science knowledge learning,community communication,etc.It meets users' learning needs for popular science knowledge.