Evaluation of College Students' IPE Environment Based on User Rating Sparsity and Big Data Technology

Abstract

Big data improves opportunities for enhancing and improving university students' IPE. In order to improve the accessibility of IPE for university students, this study integrates big-data techniques into the IPE (Ideological and Political Education) model of university students and builds the IPE platform. This study presents the idea of user rating sparsity and employs a two-step training strategy to address the issues of user cold start and sparse data in light of the drawbacks of conventional methods. This algorithm produces a very small data structure, which saves a lot of storage space. This study also uses hybrid recommendation technology, which effectively enables platform users to select customized update resources based on their interest information. According to test results, this method's suggestion accuracy can reach 95.69%, and it has a high user rating. This demonstrates that the method is reliable and accomplishes the desired result. This paper fully utilizes mega data to improve the accessibility of IPE for university students.