Pervasive cloud computing of Individualized recommendation method of characteristic service of education in post-epidemic era

Abstract Individualized recommendation of political education characteristic services in post-epidemic era is to recommend suitable political education characteristic services to students through evaluation and judgment of students' learning interests and specialties. Therefore, this paper puts forward the Individualized recommendation method of political education characteristic services in post-epidemic era. Firstly, taking the information management platform as the structural model, this paper constructs students' scoring model and data homomorphic distribution attribute model for political education characteristic services. Using the method of analyzing the distribution characteristics of teaching resources to build a Individualized recommendation model, allocate personalized features and extract fitness parameters, so as to realize the individualized recommendation of political education characteristic services in post-epidemic era. The simulation results show that this method improves the score of recommended items and reduces the average absolute error and root mean square error of the score of recommended items. It improves the quality of dynamic and accurate matching of political education characteristic services and personalized differences in post-epidemic era.