Course recommendation method and system based on graph convolutional neural network and dynamic weights

Abstract

The invention discloses a course recommendation method and system based on a graph convolutional neural network and dynamic weight, and the method comprises the steps: obtaining a score value of a user for each course, carrying out the preprocessing of the score value, and obtaining a first user-course matrix; constructing a graph convolutional neural network according to the first user-course matrix; generating a user embedding vector and a course embedding vector according to the graph convolutional neural network; predicting a score value of an unscored course in the first user-course matrix according to the user embedding vector and the course embedding vector; filling the predicted score value into the first user-course score matrix to obtain a second user-course score matrix; and performing sequence pattern mining on the second user-course scoring matrix to obtain a recommended course sequence of each user. The method improves the prediction speed, guarantees the accuracy of a prediction result, and can be widely used in the technical field of deep learning.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a course recommendation method and system based on a graph convolutional neural network and dynamic weights. Background technique [0002] In today's society, the advancement and popularization of information and communication technology has had a huge impact and development on the educational environment. As one of the fastest-growing and fastest-spreading fields, the online education system has widely affected our lives. As the online education system becomes popular, the number of students and courses on the system is growing rapidly, so how to make students better choose courses that are more interesting, more suitable for students' characteristics, rich in knowledge, and comprehensive in knowledge, It has become a problem that has attracted wide attention today, namely, the problem of course recommendation and the problem of course path recommendation. In order to sol...

AI Technical Summary

Problems solved by technology

The decision tree model uses the idea of ​​probability to determine the probability that the expected value of the net present value is greater than or equal to zero, but it is difficult to solve the problem that the number and nature of the courses and users of the system will change dynamically and the speed of change will cause the model to be inaccurate; BP neural network uses depth The idea of ​​learning to realize the prediction of the target value, but its learning speed is slow and the input parameters do not have a systematic theoretical method, and the prediction effect is unstable; Graph Convolutional Network, as a deep learning method, considers The topological relationship between data, combined with the characteristics of users and courses, effectively explores the deep and important potential relationship between the two, and the training speed is faster than BP neural network, which can effectively consider global information and local information. and effectively address such recommended technical barriers

Images