A learning resource recommendation method combining user sequential interaction with collaborative filtering

Modelling, Identification and Control / 827: Computational Intelligence ◽

10.2316/p.2015.827-015 ◽

2015 ◽

Author(s):

Wenjuan Niu ◽

Zhendong Niu ◽

Shiping Tang ◽

Zhi Huang ◽

Wei Wang ◽

...

Keyword(s):

Collaborative Filtering ◽

Learning Resource

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Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering

Mobile Networks and Applications ◽

10.1007/s11036-020-01673-6 ◽

2020 ◽

Author(s):

Honggang Wang ◽

Weina Fu

Keyword(s):

Collaborative Filtering ◽

Personalized Learning ◽

Learning Resource

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A learning resource recommendation algorithm based on online learning sequential behavior

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319400010 ◽

2019 ◽

Vol 17 (02) ◽

pp. 1940001 ◽

Cited By ~ 3

Author(s):

Xuebin Wang ◽

Zhengzhou Zhu ◽

Jiaqi Yu ◽

Ruofei Zhu ◽

DeQi Li ◽

...

Keyword(s):

Online Learning ◽

Collaborative Filtering ◽

Clustering Algorithm ◽

Recall Rate ◽

Learning Resources ◽

Recommendation Algorithm ◽

Sequential Behavior ◽

Learning Resource ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm

The accuracy of learning resource recommendation is crucial to realizing precise teaching and personalized learning. We propose a novel collaborative filtering recommendation algorithm based on the student’s online learning sequential behavior to improve the accuracy of learning resources recommendation. First, we extract the student’s learning events from his/her online learning process. Then each student’s learning events are selected as the basic analysis unit to extract the feature sequential behavior sequence that represents the student’s learning behavioral characteristics. Then the extracted feature sequential behavior sequence generates the student’s feature vector. Moreover, we improve the H-[Formula: see text] clustering algorithm that clusters the students who have similar learning behavior. Finally, we recommend learning resources to the students combine similarity user clusters with the traditional collaborative filtering algorithm based on user. The experiment shows that the proposed algorithm improved the accuracy rate by 110% and recall rate by 40% compared with the traditional user-based collaborative filtering algorithm.

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