An Online Course Recommendation Model Integrating Knowledge Subgraph and Attention Mechanism
The recommendation system can help users filter out the items that meet their needs among the massive resources,and the evolving recommendation system provides new ideas for online education.As an important part of online education,online course resource recommendation currently has the problems of overload of course resources and lack of interpretability of course recommendation results.In this regard,we propose an online course recommendation model based on knowledge subgraph and attention mechanism to use knowledge subgraph for recommendation.Different from the model that directly uses the knowledge graph for recommendation and ignores the problem of inaccurate knowledge representation,the proposed model first uses the Node2vec random walk method to extract the connected subgraph connecting user-course pairs from the knowledge graph,and then encodes the subgraph through the hierarchical attention network to generate a subgraph embedding for the prediction of the courses required by the user.Finally,a list of Top-N recom-mended courses is generated,and an interpretability description of the proposed model is given.In order to verify the effectiveness of the proposed model,the data set on the"MOOC(MOOC)of Chinese universities"was used as the sample,and the experimental results show that compared with the KGCN-PN,GAT,KGAT and POCR,the proposed model improves the NDCG,HR and MRR evaluation indexes by 10.6％,9.41％and 13.7％,respectively.
knowledge subgraphhierarchical attention mechanismrecommendation systemonline coursesrandom walk
万方数据
维普
