A recommendation method of teaching resources based on similarity and ALS

Abstract

This paper provides a collaborative filtering recommendation algorithm based on the combination of item similarity and alternating least square collaborative filtering based on the Spark platform. This is a recommended method to improve the efficiency of prediction calculations and reduce system response time. In order to solve the problem of model inaccuracy caused by the sparse data of the existing collaborative filtering recommendation scheme, which leads to the inaccuracy of recommending suitable online education and teaching resources to different users, the present invention uses the least squares collaborative filtering recommendation algorithm on the Spark big data analysis platform Optimize and use, and then use parallel methods to increase the amount of work completed per unit time and the accuracy of recommendations, and solve the problem of inaccurate recommendation of teaching resources.