Personalized Recommendation Method for English Teaching Resources Based on Artificial Intelligence Technology

Abstract

Abstract Traditional recommendation methods decompose keywords and key sentences less frequently, resulting in low accuracy of recommended content. Therefore, a personalized recommendation method for English teaching resources based on artificial intelligence technology is proposed. Web mining technology is used to collect user personalized data and mine teaching resource rules and patterns that can represent user characteristics. Artificial intelligence technology is used to simulate the intelligent behavior of users searching for resources, to obtain words with a higher frequency. The words with higher relative contribution are selected as keywords, thereby calculating the similarity between keywords and user characteristics, then the teaching resources with high similarity are taken as the recommendation target, and the recommended format is selected to implement resource recommendation. Experimental results show that compared with traditional methods, this method improves the accuracy of recommended content and enables teaching resources to better meet the individual needs of users.