Research on English Reading Comprehension Material Recommendation System under Text Similarity Algorithm - Chu, Ruili (Applied mathematics and nonlinear sciences, 2025)
With the development of information technology and the change of people’s education concept, personalized learning is getting more and more attention. The traditional classroom is difficult to realize the need of personalized English reading comprehension material recommendation, this paper designs a fusion English reading comprehension material recommendation system based on collaborative filtering algorithm and improved text similarity algorithm. Aiming at the problem that the traditional text similarity algorithm ignores the user’s differentiated attention to the information of English reading comprehension materials in text matching, the Inter-TF-IDF algorithm, which integrates the calculation of concentration and dispersion, is proposed. Combining this algorithm and the user-based collaborative filtering algorithm, the fusion recommendation system of this paper is proposed. The system utilizes the collaborative filtering algorithm to get the preliminary recommendation results of English reading comprehension materials, and then utilizes the improved Inter-TF-IDF algorithm to calculate the similarity between the text of the English reading comprehension materials and the text browsed by the user, and selects the English reading comprehension materials with a high degree of similarity as the final recommendation results. The overall recommendation accuracy of the system in this paper is maintained at a high level of 0.82-0.95. In the actual application effect, it significantly improves the English scores of students using the system, and obtains a high degree of satisfaction from the students. The recommendation system in this paper has a good promotion and application prospect in the field of English learning.