Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 172-177.

• Data Science • Previous Articles     Next Articles

Personalized Question Recommendation Based on Autoencoder and Two-step Collaborative Filtering

XIONG Hui-jun, SONG Yi-fan, ZHANG Peng, LIU Li-bo   

  1. (School of Information Engineering,Ningxia University,Yinchuan 750021,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: Personalized question recommendation is an effective way to improve learning efficiency.It helps students get rid of the “Massive Questions” and has important significance to achieve adaptive teaching and promote education equity.However,most of the personalized question recommendation methods are based on collaborative filtering without focusing on the knowledge points,which causes the problem that the positioning of the recommended questions are inaccurate.In order to solve this problem,a personalized question recommendation system based on deep autoencoder and a two-step collaborative filtering was adopted in this paper.Firstly,considering students’ master degree of knowledge points,the two-step collaborative filtering question recommendation based on knowledge points is realized.Secondly,item response theory and deep autoencoder are used to predict the scores and the comprehensive scores of the students involving recommended knowledge points on the recommended questions.Finally,the prediction results are synergistically decided,the difficulty of the final personalized recommendation questions is controlled,and a list of final recommended questions in generated.Comparison experiments verify that the recommended results of the proposed recommendation method are more personalized and accurate than that of traditional question recommendation methods.

Key words: Auto encoder, Collaborative filtering, Deep learning, Personalized learning, Personalized question recommendation

CLC Number: 

  • TP301
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