The network formed by the online education platform is characterized by a large amount of data,rich entity types,and complex relationships.On the one hand,online education is being popularized,but on the other hand,online courses are facing the problems of low utilization,low completion,and high dropout rates.Personalized course recommendations are conducive to improving students'enthusiasm for learning.Among these,whether courses can be successfully completed is an important factor that students consider when selecting courses.Considering this,this study proposes a personalized course recommendation model based on the prediction of learning completion.This approach models the students'course learning session graph,and generates their learning status representations according to their course learning sequence and the completion of the course.Simultaneously,considering the influence of online learning environment factors on courses,a heterogeneous graph of online course learning is constructed,and a graph neural network is used to generate the embedding of course nodes in the graph.Thereafter,the course embeddings are fused with the students'learning status representation and the embedding of courses through an interactive mechanism to predict their degree of completion of the next course they will take.Finally,the courses are sorted according to the degree of recommendation completion.The experimental results on three large-scale online course learning datasets,namely CNPC,HMXPC,and Scholat,demonstrate that the model can effectively improve the accuracy of recommendations,and has significantly improved both the NDCG and MRR metrics compared to the baseline model optimal results.When K of the evaluation index is 5,NDCG@5 is improved by 21.08％,17.73％,and 5.41％,respectively,and MRR@5 is improved by 25.66％,31.59％,and 26.96％,respectively.
维普
万方数据
