Application research of personalized recommendation system for English learning resources based on GloVe

Abstract

With the rapid development of information technology, personalized learning resource recommendation systems have shown great potential in English learning. However, current English learning resources are abundant but scattered, making it difficult for learners to obtain learning materials that efficiently meet their needs. Based on this, this study constructed a personalized recommendation system that integrates the GloVe model to capture words' distributed representation, effectively improving resource recommendations' accuracy and relevance. The recommendation system preprocesses large-scale English learning resources and generates personalized resource recommendation lists based on learners' historical learning behaviors and preferences. During the experiment, a three-month test was conducted on 100 learners with different levels of English proficiency, and corresponding usage feedback and learning effectiveness data were collected. The experimental results showed that compared to traditional recommendation methods, the recommendation system based on GloVe improved resource matching by 35% and learner satisfaction by 40%. A survey found that 80% of learners reported that recommended resources better met their learning needs, and their learning efficiency increased by an average of 25%. The system also promoted the growth of learners' vocabulary. The experimental group learners increased their vocabulary by an average of 1500 words within 3 months, significantly higher than the 800 words in the control group. The personalized recommendation system for English learning resources based on GloVe has shown excellent performance in improving the matching of learning resources, learner satisfaction, and learning outcomes, providing strong support for English learning.