English learning video recommendation approach incorporating user preference differences and sequence recommendation

The experiment proposes an English learning video recommendation method based on user preference differences with sequential recommendation. The experiment first introduces a collaborative filtering algorithm to preprocess user behavior and filter abnormalities and mutations in user behavior to further improve the accuracy. Then a deep learning algorithm for sequence recommendation based on user behavior is proposed, which mainly uses short-term user behavior for learning and representation. In addition, a model based on the attention mechanism is introduced to represent the long- and short-term user behaviors. At the same time, the difference between the long- and short-term behaviors is utilized for selective learning, which solves the need of identifying the change of user interests in educational scenarios. The results indicated that when the number of iterations reaches 250, the research method has a minimum loss value of 0.658. In the comparison of model accuracy, at the 50th iteration, the accuracy of the constructed method is as high as 94.89%. In the comparison of recommendation time, when the data volume is 4 MB, the recommended time of the research method is 0.072 s. When the data volume is 20 MB, the recommended time of the research method is always less than 0.100 s. The results indicate that the research method is the most effective approach for creating English learning videos. Furthermore, the reliability of data transmission is consistently high, ensuring that students’ online learning needs are met with accuracy.