Optimizing Personalized Recommendation of College English Learning Resources Using Recommender System Algorithms

Abstract

This study investigates the optimization of personalized recommendation of university English learning resources using recommender system algorithms. With the increasing demand for tailored educational experiences, particularly in the realm of language learning, personalized recommendation systems offer immense potential to enhance the efficacy of English language instruction in university settings. Through a systematic exploration of different recommender system algorithms, including collaborative filtering and content-based filtering approaches, they evaluate their performance in delivering personalized learning materials to individual learners. The findings reveal a trade-off between precision and recall, with collaborative filtering algorithms excelling in recommending highly relevant items while content-based filtering approaches offer a more comprehensive coverage of relevant materials. Statistical significance tests confirm the superiority of content-based approaches in optimizing personalized recommendation of university English learning resources. These insights underscore the importance of leveraging advanced computational techniques to address the diverse needs and preferences of learners and pave the way for more efficient and effective English language instruction in university settings.