A Novel Framework With Weighted Heterogeneous Educational Network Embedding for Personalized Freshmen Recommendation Under the Impact of COVID-19 Storm

Abstract

The global explosion of COVID-19 has brought unprecedented challenges to traditional higher education, especially for freshmen who have no major; they cannot determine what their real talents are. Thus, it is difficult for them to make correct choices based on their skills. Generally, existing methods mainly mine isomorphic information, ignoring relationships among heterogeneous information. Therefore, this paper proposes a new framework to give freshmen appropriate recommendations by mining heterogeneous educational information. This framework is composed of five stages: after data preprocessing, a weighted heterogeneous educational network (WHEN) is constructed according to heterogeneous information in student historical data. Then, the WHEN is projected into different subnets, on which metapaths are defined. Next, a WHEN-based embedding method is proposed, which helps mine the weighted heterogeneous information on multiple extended metapaths. Finally, with the information mined, a matrix factorization algorithm is used to recommend learning resources and majors for freshmen. A large number of experimental results show that the proposed framework can achieve better results than other baseline methods. This indicates that the proposed method is effective and can provide great help to freshmen during the COVID-19 storm.