English Reading Recommendation Algorithm Based on Big Data Corpus

Abstract

In today's digital age, the abundance of online content poses both opportunities and challenges for readers seeking personalized reading recommendations. This paper presents an innovative approach to address this issue through the development of an English reading recommendation algorithm leveraging a vast corpus of big data. By harnessing the power of natural language processing (NLP) and machine learning techniques, the algorithm analyzes textual data from diverse sources, including books, articles, blogs, and academic papers The algorithm employs sophisticated algorithms to extract semantic meanings, identify patterns, and understand user preferences. Through a combination of collaborative filtering, content-based filtering, and hybrid recommendation techniques, it generates tailored reading suggestions that align with individual interests, reading habits, and proficiency levels. Furthermore, the algorithm dynamically adapts to evolving user preferences and feedback, ensuring the relevance and accuracy of recommendations over time. To evaluate the effectiveness of the approach, extensive experiments were conducted using a large-scale dataset comprising diverse literary genres, topics, and writing styles. The results demonstrate significant improvements in recommendation accuracy and user satisfaction compared to conventional methods. Additionally, the algorithm's scalability and efficiency were validated through performance benchmarking tests on real-world datasets. Overall, the English reading recommendation algorithm represents a promising solution for enhancing the reading experience in the digital era. By leveraging the rich insights derived from big data, it empowers readers to discover engaging and relevant content tailored to their unique preferences, ultimately fostering a deeper appreciation for literature and knowledge acquisition.