Interactive AI Virtual Teaching Resource Intelligent Recommendation Algorithm Based on Similarity Measurement on the Internet of Things Platform

Abstract

Abstract To tackle the issues of low recommender system correctness and the long time of recommendation in the traditional intelligent recommendation algorithm of interactive artificial intelligence (AI) virtual teaching resources, a similarity measurement-based intelligent recommendation algorithm of interactive AI virtual teaching resources is proposed. According to the contact frequency of adjacent learners, the behavior patterns of learners are mined, and the user characteristics are selected on the basis of the mutual information feature selection method. In the context of the category attribute matrix and learner category attribute scoring matrix of interactive AI virtual teaching resources, the user interest matrix is constructed by the Relevance Feedback based on Inverse Learning Function (RF-ILF) method. The user similarity is calculated and the neighbor set is found in the similar user clusters after clustering. According to the results of the simulation, the suggested algorithm is more efficient and provides more accurate recommendations when recommending interactive AI virtual teaching resources.