User interest modeling and collaborative filtering algorithms application in English personalized learning resource recommendation

In order to solve the problem that the current traditional English classroom teaching in large classes has higher teaching efficiency, but it is difficult to take care of every student, and it is difficult to meet the needs of students' personalized learning, this paper combines user interest modeling and collaborative filtering algorithms to propose a knowledge-oriented learning resource recommendation method. Moreover, this paper proposes an algorithm based on user interest model. In addition, on the basis of obtaining the user's historical interest model, this paper combines user behavior information to obtain the user interest model and calculates the similarity between the candidate items and the user, and makes TOP-N recommendation based on the similarity calculation result. Finally, this paper conducts experiments on the news dataset and compares the results with the benchmark algorithm to prove the effectiveness of the algorithm. The results show that this paper has achieved certain results in the research of combining multi-data source user interest modeling and recommendation and has contributed to the research of interest modeling.