Design And Implementation Of Learning Information Recommendation System For Middle School Students Based On Deep Learning

With the development of science and technology,more and more information is spreading on the network,and the problem of information overload has followed.Although the two solutions of classification directory and search engine can help users to achieve fast search to a certain extent,with the increasing amount of data,these two methods are not able to serve users well.The generation of the recommendation system makes up for the shortcomings of the classification catalogue and the search engine,and can implement active recommendation based on the user's historical behavior,and provide users with information that they are interested and useful.At present,there are many middle school students' learning materials on the Internet,including learning methods,learning objectives,knowledge development and other information.No matter for students,parents or teachers,there is no complete website that can integrate these learning materials together,and there is no website that can apply the recommendation algorithm to them.Faced with so many information about middle school students,the recommendation algorithm is applied to the information website,and different recommendation lists are generated for different objects,which can effectively improve the user experience.In recent years,the rapid development of deep learning has achieved very good results in the field of computer vision and natural language processing.Therefore,combining deep learning with traditional recommendation algorithms to achieve a deep learning-based middle school student information website has very important practical significance.In order to realize the middle school student information website based on deep learning,this paper mainly studies the following contents:1.This paper introduces the research background and significance of the recommendation system,and then introduces the traditional recommendation algorithm based on content recommendation algorithm,memory-based recommendation algorithm,model-based recommendation algorithm and hybrid recommendation algorithm,and introduces the advantages and disadvantages of the recommended algorithm,and finally the calculation of the similarity commonly used in the recommendation algorithm.2.This paper elaborates the neural collaborative filtering(NCF)algorithm,introduces the shortcomings of the multi-layer perceptron used in the algorithm,and improves the network structure in the algorithm.The idea of combining residual network and neural collaborative filtering algorithm is proposed for the first time,and a new recommendation model ResnetNCF based on residual network is proposed.The proposed ResnetNCF algorithm was implemented using the Python 3 language and the deep learning framework Tensorflow,and the experimental results were compared and analyzed on the public datasets MovieLens 1M and Yelp commonly used in the recommended models.The experimental results show that the ResnetNCF method has improved performance index compared to other algorithms.3.This paper implements a deep learning-based middle school student learning information website,including recommendation,search,discovery,class classification and other modules,and tests the main functional modules in the website.And the ResnetNCF method proposed in this paper is applied to the recommendation module on the website to form a personalized information recommendation website,which can better improve the recommendation effect.