Personalized Recommendation Technology of Network Teaching Resources Based on Ant Colony Algorithm

Abstract

In order to solve the problem of low recall rate in traditional network teaching resources personalized recommendation technology, an ant colony algorithm-based network teaching resources personalized recommendation technology was designed. By describing the user’s online teaching resource interest, the user’s online teaching resource interest is acquired, and the ant colony algorithm is used to dynamically adjust the user’s online teaching resource interest to obtain information that the user is interested in, that is, the user’s personalized characteristics, and to generate a synthesis User interest models, including individual user models, group user interest models, and integrated user interest models, build a personalized recommendation model for online teaching resources, including the application layer, business logic layer, and data layer, to achieve personalized recommendation for online teaching resources. In order to prove the high recall rate of the personalized recommendation technology of network teaching resources based on ant colony algorithm, the traditional personalized recommendation technology of network teaching resources was compared with this technology. The experimental results show that the recall rate of this technique is higher than that of the traditional personalized recommendation technique.