Personalized Recommendation of English Online Teaching Content Based on Logistic Regression Algorithm

Abstract

English online teaching content encompasses a wide range of materials designed to facilitate language learning in digital environments. These resources include interactive lessons, video tutorials, audio recordings, quizzes, vocabulary exercises, grammar explanations, and authentic texts such as articles, short stories, and dialogues. The content aims to cover various language skills, including listening, speaking, reading, and writing, catering to learners of different proficiency levels and learning styles. This paper introduces a Cross-Domain Personalized Recommendation System (CDPRS) designed to enhance the delivery of English language teaching content. Leveraging machine learning algorithms and data analytics techniques, the CDPRS tailors recommendations to individual user preferences, learning objectives, and proficiency levels across diverse content domains. Through a series of analyses and evaluations, including regression analysis and simulation studies, the effectiveness and utility of the CDPRS are demonstrated. Through a series of analyses and evaluations, including regression analysis and simulation studies, the effectiveness and utility of the CDPRS are demonstrated. Results indicate that the system achieves high levels of accuracy (mean accuracy = 0.85), diversity (mean diversity = 0.72), novelty (mean novelty = 0.78), and user satisfaction (mean satisfaction = 0.82) in providing personalized recommendations.