Impact of ID3 Algorithm-based Reading Resource Recommendation Model on College English Reading Teaching

English reading is crucial for college and university English education. Hence, new methods, such as adaptive recommendations of reading resources, are needed to improve teaching quality. The study establishes a recommendation model that classifies resources and students in both directions. The ID3 algorithm was improved by introducing correlation coefficients to enhance the information gain formula. An English training institution training course was selected as the subject. The performance test results show that the performance of the recommendation model based on the improved ID3 algorithm is better than the standard ID3 algorithm and the traditional recommendation model performance. The average score of students improved to 18.01±1.07, and the satisfaction rate of students was 91.22%. The model evaluated using four indicators revealed improved performance. The results show that students with strong reading ability are more sensitive to the difficulties of reading resources, while students with relatively weak ability should focus on the topic and type of content.