Research And Design Of Personalized Learning Recommendation System Based On LSTM

With the continuous expansion of the demand for building a learning society,personalized learning is gradually considered to be the ideal choice for future education.However,with the explosive growth of network data,its structure is becoming more and more complex,and more and more information is flooded in the network environment,which makes learners face the trouble caused by "information overload".The recommendation system represented by personalized recommendation technology can effectively solve this problem and meet the individual needs of learners.In order to study the principle and application process of the recommended technology,the core of the recommended technology and the implementation process are first understood through literature reading.Research shows that the two major bottlenecks faced by collaborative filtering in the recommendation process are "data sparsity" and "cold start".For the insufficiency of the algorithm,most of the solution strategies are to optimize the algorithm,but it can not fundamentally solve the shortcomings of the algorithm.With the development of artificial intelligence,intelligent recommendation based on deep neural network is considered to be the direction of future development.Long Short Term Memory(LSTM)Recurrent Neural Network is a type of Recurrent Neural Network(RNN).Research shows that LSTM is a problem in sequence prediction and captures the evolution of user taste.There is a good output on the issue.Therefore,the study of the LSTM-based recommendation compared with the traditional collaborative filtering recommendation algorithm is of great significance to how to solve the recommendation in the context of large data volume.In view of the problem of "information overload" and the increasing amount of data faced by online education,the 2010 International Knowledge Discovery and Data Mining Contest Data Set(KDDCUP)was selected to conduct an algorithmic experimental study.This dataset is consistent with the research of this topic,both in terms of data volume and source background.Firstly,the recommendation based on collaborative filtering was studied.Based on the recommendation effect evaluation index,the problems were found.Keep up with the frontier of the era and study recommendations based on the Deep Neural Network Model(LSTM),compared with the recommendation performance of the traditional recommendation algorithm.Finally,developing a personalized online learning system for trainees based on the above problems,selecting the optimal recommendation algorithm,and serving the above research results to the students to learn,has important practical significance for improving student learning efficiency and solving the problem of "information overload".