Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

Algorithm 1

Input: College student scoring matrix and the number of features;

Output: personalized recommendation of the square error approximation matrix ;

To analyze the time complexity of the personalized recommendation algorithm for higher education resources, is assumed to represent the number of features, to represent the number of iterations, and and to represent the number of college students and the number of recommended objects, respectively [36]. The time complexity of the personalized recommendation algorithm for higher education resources is calculated by using a standard matrix operation.