Personalized Recommendation Method of Maternal and Child Health Education Resources Based on Association Rule Mining Algorithm

Abstract

Some personalized recommendation methods of maternal and child health education resources have the problem of long response time of resource recommendation. A personalized recommendation method of maternal and child health education resources based on association rule mining algorithm is designed to improve the above defects. Automatically obtain the text content within the marking range, obtain the characteristics of digital teaching resources, use the session log to record various behaviors of users, build the user interest preference model, extract the core language concept knowledge in the language concept lattice, establish the maternal and child health knowledge base, calculate the maximum likelihood function of the ability parameter, and design the personalized recommendation method based on the association rule mining algorithm. Experimental results: the response times of the personalized recommendation method of maternal and child health education resources in this paper and the other two personalized recommendation methods of maternal and child health education resources are 5.055s, 7.119s and 7.508s respectively, which shows that the personalized recommendation method of maternal and child health education resources in this paper is more feasible after fully combining the association rule mining algorithm.