Application of Conditional Random Field Model Based on Machine Learning in Online and Offline Integrated Educational Resource Recommendation

Abstract

It is of great significance to mine the learning resources that learners are interested in from massive data and recommend appropriate educational resources to them according to the characteristics of students. To improve the accuracy of educational resource recommendation, this paper proposes an educational resource recommendation system based on a graph attention network and conditional random field fusion model. It builds all comments for each student and educational resource into a comment graph. Through the graph's topological structure to capture the network and the dependency between words in the commentary text, the adjacency information of each node is aggregated by the graph attention network based on connection relation. After the graph attention network layer, the conditional random field inference layer is added. The label sequence with the highest probability is output by the dependent random field inference layer, which is taken as the final recommendation result of the model. Experimental results show that the proposed algorithm has better performance in accuracy and diversity than the traditional recommendation algorithm.