Personalized resource recommendation method of student online learning platform based on LSTM and collaborative filtering

Abstract

Abstract Online education resources are more and more abundant, which brings some challenges to learners’ personalized selection. How to provide personalized recommendation services from massive resources according to the needs of learners has gradually become the focus of scholars’ research. Therefore, this article improves the traditional collaborative filtering recommendation algorithm and constructs a personalized recommendation model of an online learning platform based on a long-term memory network and collaborative filtering. First, the stack noise reduction autoencoder combined with auxiliary information is used to extract the user potential vector, and the project potential vector is extracted by using the short-duration memory network and the attention mechanism. Then, the double attribute scoring matrix is used to divide the attributes, and the backpropagation network is used to predict the scores. Through the experimental analysis, the hit rate and recall rate of the model constructed by the research institute are 0.7548 and 0.7247, respectively, and the cumulative gain of normalized loss and running time are 0.3385 and 2.72 s, respectively. This model can effectively make up for the defects of the traditional algorithm caused by cold start and sparse score data and provide more effective and high-quality learning resource recommendations for students.