A personalised recommendation of mobile learning model based on content awareness

In order to overcome the problems of traditional recommendation methods such as large error in recommendation results and long time-consuming process of recommendation results generation, the paper proposes a personalised recommendation method based on content-aware mobile learning mode. First, the recommendation process architecture is designed, which mainly includes a user demand analysis module, a user preference analysis module, and a mobile learning model resource library decision module. Then, the energy function is used, and the dataset is inserted to design the content perception process. Finally, according to the perceptual results, a user emotional topic model with a supervision mechanism is used to complete personalised recommendation. The experimental results show that the average absolute error value of the recommendation results obtained by the method in this paper is between 0.06-0.15, the maximum recommendation result generation process takes only 4.5 s, and the clustering effect of different mobile learning modes is better.