Research And Implementation Of Educational Resource Recommendation System Based On α-divergence And Improved Random Forest

In recent years,with the popularization of network technology and the rapid development of education industry,more and more teaching content is gradually transmitted through online teaching.In the context of big data,the trend of network teaching gradually appears.How to meet the needs of students and users in high efficiency and stability,especially in the case of information overload,how to accurately recommend relevant educational resources for students,has become a problem that needs to be solved in online teaching.And in the field of recommendation,how to effectively determine the similarity is the core issue of collaborative filtering technology,because the similarity not only determines the selection of neighbor users,but also has a decisive impact on the quality of recommendation.At the same time,how to use high quality optimization algorithm to select candidates becomes an important strategy.Based on the above theories,this paper carries out the following work:Firstly,from the perspective of theoretical learning,this paper analyzes domestic and foreign research papers on the basis of mobile terminal learning,small program development and recommendation algorithm.Then combined with the actual situation,the recommendation algorithm used by the popular educational apps and small programs is analyzed.On this basis,the requirements of the online education system are analyzed,and the basic structure of the system is designed,and the functions of the system need to be realized are established.Secondly,in order to better recommend relevant educational resources to users,this article is based on the differences between two signal sources α-divergence Improve the divergence similarity algorithm.Firstly,construct a user rating matrix and calculate α-divergence similarity,subsequently based on α-divergence,A formula is proposed to combine the inherent characteristics of divergence with the similarity of course labels,and a method is proposed to α-divergence,A scheme combining divergence similarity with course label similarity is proposed to recommend candidate options using this similarity,thereby ensuring the accuracy of similarity.At the same time,in terms of optimization,starting from the user dimension,the random forest model is trained to conduct secondary optimization operations on primary candidates,so as to better recommend educational resources for users through the user’s personal characteristics.At the same time,within the random forest model,a random forest weighted model based on boundary values and secondary training is proposed to increase the accuracy of random forest classification and improve the recommendation accuracy of the system.Finally,the recommendation algorithm proposed in this article was applied to an online education system,and an online education system was designed and implemented.According to the actual requirements of the algorithm,the recommendation system module and other related functions have been implemented.The client is mainly developed based on We Chat mini programs,which mainly enhance user convenience and enable users to learn the resources they need anytime and anywhere.The test proves that the basic function of the system is perfect,the system runs stably,and can meet the user’s preference for learning resources.