Cross-Domain Personalized Learning Resources Recommendation Method

Abstract

According to cross-domain personalized learning resources recommendation, a new personalized learning resources recommendation method is presented in this paper. Firstly, the cross-domain learning resources recommendation model is given. Then, a method of personalized information extraction from web logs is designed by making use of mixed interest measure which is presented in this paper. Finally, a learning resources recommendation algorithm based on transfer learning technology is presented. A time function and the weight constraint of wrong classified samples can be added to the classic TrAdaBoost algorithm. Through the time function, the importance of samples date can be distinguished. The weight constraint can be used to avoid the samples having too big or too small weight. So the Accuracy and the efficiency of algorithm are improved. Experiments on the real world dataset show that the proposed method could improve the quality and efficiency of learning resources recommendation services effectively.