基于深度自编码器和二次协同过滤的个性化试题推荐方法

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

个性化试题推荐是实现高效学习的有效途径,帮助学生从“题海战术”中解脱出来,对实现适应性教学、促进教育公平具有重要意义。但目前个性化试题推荐方法大多是基于协同过滤进行试题层面的个性化推荐,没有聚焦到知识点层面,存在推荐试题定位不准确的问题。针对上述问题,对基于深度自编码器和二次协同过滤的个性化试题推荐方法进行了研究。首先考虑到学生对知识点的认知情况进行基于知识点的二次协同过滤试题推荐,然后应用项目反应理论和深度自编码器来预测学生在推荐试题上涉及推荐知识点的得分以及综合得分,最后对预测结果协同判断并控制最终个性化推荐试题的难度,产生最终的推荐试题列表。通过对比实验验证提出的推荐方法的推荐结果相对于传统试题推荐更具个性化和准确性。

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.