Learning Resource Recommendation Based on Generalized Matrix Factorization and Long ShortTerm Memory Model Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally canu0027t deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named GLSTM. Our model integrates the Generalized Matrix Factorization (GMF) with Long ShortTerm Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy.

Constrained Matrix Factorization for Course Score Prediction Recommender system is widely used in ecommercial platforms to recommend users suitable items according to usersu0027s preferences. In recent years, an increasing amount of attention has been paid to the application of recommender system in education. There are many online learning systems that can recommend students suitable courses according to studentsu0027 learning performances. However, there are few universities using recommender system to recommend students suitable elective courses. It is generally known that students in higher grade take the courses earlier than those in lower grade. Therefore, the elective course scores of sophomores can be predicted by using the course score information from students of higher grades. However, the unbalanced distribution of courseenrollment data makes it hard to predict the scores of the courses that are in a low selection rate. Therefore, we propose a Constrained Matrix Factorization (ConMF) algorithm to predict sophomoresu0027 elective course scores, which integrates the course average score into the objective function so as to make up the prediction deviation caused by the unbalanced course selection rate and make more accurate prediction than the traditional Matrix Factorization (MF) approach. The experimental results show that our proposed model outperforms the stateoftheart methods in the task of university studentsu0027 course score prediction.