The problem of low relevance of recommendations due to the large amount of data on online teaching cloud platforms.This article proposes a personalized recommendation method for online teaching cloud platform resources based on user behavior characteristics.By collecting historical data to filter key features,combining K-center clustering algorithm to mine user behavior characteristics,establishing association rules with resource features,and combining preference factors,a personalized resource recommendation list is generated for users.Experimental testing shows that this method has high recommendation relevance and meets the practical application needs of the platform.
维普
万方数据