Computer Science ›› 2019, Vol. 46 ›› Issue (11): 235-240.doi: 10.11896/jsjkx.180901827

• Artificial Intelligence • Previous Articles     Next Articles

Questions Recommendation Based on Collaborative Filtering and Cognitive Diagnosis

QI Bin, ZOU Hong-xia, WANG Yu, LI Ji-xing   

  1. (School of Space Information,Space Engineering University,Beijing 101416,China)
  • Received:2018-09-30 Online:2019-11-15 Published:2019-11-14

Abstract: The question recommendation method isthe new application of data mining on the Education Measurement,which is an important performance of the intelligence and personalization in the intelligent education,particularly.At present,there are two types of mainstream test recommendation methods,including the question recommendation based on collaborative filtering and the question recommendation based on cognitive diagnosis.However,the former ignores the knowledge attribute of independent individuals,the latter is lack of the common evaluation.In order to improve the accuracy and efficiency of the question recommendation,comprehensive considering the knowledge attributes of the independent testing subjectand the knowledge commonality of the environment-like groups,this paper proposed a testing recommendation method based on collaborative filtering and cognitive diagnosis.Firstly,the proposed method designs a cognitive diagnosis model based on multi-level attributes scoring,which is used to model the subject’s answer.Then,the subject’s knowledge attribute is used for probabilistic matrix factorization to predict the potential answers.Finally,the appropriate questions are recommended to the subjects according to the information value.The testing recommendation comprehensively improves the interpretability and reliability that the experiment shows the method improves the efficacy by 20.35% and 2.5% respectively compared with collaborative filtering and cognitive diagnosis.

Key words: Collaborative filtering, Cognitive diagnosis, Questions recommendation, Cognitive diagnosis model, Information value, Data mining

CLC Number: 

  • TP301
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