Aiming at the problem that the influence of time and space information of the location was not considered when making the location recommendation by Gated Recurrent Unit (GRU) of recurrent neural network, the spatiotemporal-aware GRU model was proposed. In addition, aiming at the noise problem generated by the unrelated check-in data in check-in sequence, the next location recommendation method of SpatioTemporal-aware GRU and Attention (ST-GRU+Attention) was proposed. Firstly, time gate and distance gate were added in the GRU model by counting the time slot and distance gap between two locations. The influence of time and space information on recommending next location was controlled by setting the weight matrices. Secondly, the attention mechanism was introduced. The attention weight coefficients of the user were obtained by calculating the attention weight scores of the user preferences, and the personalized preference of the user was obtained. Finally, the objective function was constructed and the model parameters were learned by Bayesian Personalized Ranking (BPR) algorithm. The experimental results show that the accuracy of ST-GRU+Attention is improved significantly compared to the recommendation methods of Factorizing Personalized Markov Chain and Localized Region (FPMC-LR), Personalized Ranking Metric Embedding (PRME) and Spatial Temporal Recurrent Neural Network (ST-RNN), and the precision and recall of ST-GRU+Attention are increased by 15.4% and 17.1% respectively compared to those of ST-RNN which is the best of the three methods. The recommendation method of ST-GRU+Attention can effectively improve the effect of next location recommendation.

In recent years, test question resources in online education has grown at an explosive rate. It is difficult for students to find appropriate questions from the mass of question resources. Many test question recommendation methods for students have been proposed to solve this problem. However, many problems exist in traditional test question recommendation methods based on unified probalilistic matrix factorization; especially information of student knowledge points is not considered, resulting in low accuracy of recommendation results. Therefore, a kind of personalized test question recommendation method based on unified probalilistic matrix factorization was proposed. Firstly, through a cognitive diagnosis model, the student knowledge point mastery information was obtained. Secondly, the process of unified probalilistic matrix factorization was executed by combining the information of students, test questions and knowledge points. Finally, according to the difficulty range, the test questions were recommended. The experimental results show that the proposed method gets the best recommedation results in the aspect of accuracy of question recommendation for different range of difficulty, compared to other traditional recommendation methods, and has a good application prospect.