Personalized information recommendation based on synonymy tag optimization

Individual recommendation method of college physical education resources based on cognitive diagnosis model

“…[10] is always the highest, indicating that this method has a high resource exposure rate and is not suitable for resource personalized recommendation. Although the resource exposure rate of the other two methods is lower than that of the method in this paper, it is still much higher than that of the method; in this paper The TOR value measured by the method in reference [8] is also significantly higher than that of other methods. Therefore, it can be seen that this method has a high test overlap rate and can not achieve accurate student cognitive diagnosis, resulting in inaccurate recommendation effect.…”

“…The formula is shown in (29): According to Figure 2, it can be seen that the recommendation error of the method in Reference [10] is the highest when the proportion of the dataset is not passed, and the highest value is 0.38, indicating that this method cannot accurately implement resource recommendation. The MAE values of the reference [8] method and the reference [9] method are similar, both higher than 0.22. However, the MAE values of the methods in this paper are all less than 0.22, indicating that the recommendation results of teaching resources are more accurate.…”

“…Regarding the experiment of the TOR indicator, the TOR value of the method in this paper is always lower than 0.01, indicating that the overlap rate of students extracting the same resources is low, and the recommendation effect is more accurate. The TOR value measured by the method in Reference [8] is also significantly higher than other methods, which cannot achieve accurate cognitive diagnosis of students, resulting in an inaccurate recommendation effect. Regarding the experiment of TE index, the method in this paper can recommend teaching resources to students with less resource consumption.…”

“…The experiment runs on Windows 10 operating system, the CPU is Intel i7-4790, the graphics card is NVIDIA GTX 1070, and the memory is 32G. This paper applies the proposed method to a learning platform of a university, makes personalized recommendation on the data set of physical education teaching resources in the university, and selects the methods in reference [8], reference [9] and reference [10] to compare with the proposed method.…”

“…Therefore, it is particularly important to pay real-time attention to and dynamically update students' cognitive level and what goals and needs students need to complete at each stage of the learning process. Many scholars have studied personalized recommendation methods, for example, Wei et al [8] studied personalized information recommendation based on synonym label optimization, but in the recommendation method, the impact of data sparsity on the recommendation effect was not considered. Wu et al [9] studied personalized recommendation based on the consumption psychology of local group purchase ecommerce users, but when new resources appeared, the lack of corresponding real-time update ability lead to inaccurate recommendation results.…”