A Baggingbased ensemble method for recommendations under uncertain rating data Matrix factorization (MF) is one of the mostused techniques to build recommender systems. However, in practical use, the existence of noise in the training set brings some uncertainty, degrading the performance of MF approaches. In this paper, we propose a Baggingbased MF framework, an ensemble method of using multiple MFbased models to improve the stability and accuracy. Specifically, our framework first rebuilds new training sets by resampling on the original ratings, then takes advantage of the sets to train MF models and finally combines the predictions from the models in a ensemble way. The experiment results on real data show that our framework can achieve some performance improvement when having noise samples.

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.