知识图与行为图混合嵌入的学习者偏好预测

doi:10.11959/j.issn.1000-436x.2021125

Abstract

Abstract:

To solve the problems of inaccurate prediction of learner preference and insufficient utilization of structural information in the knowledge recommendation model, for the knowledge structure and learner behavior structure in the learner’s preference prediction model, the model of learner preferences predication with mixture embedding of knowledge and behavior graph was proposed.First, considering using graph convolution network (GCN) to fit structural information, GCN was extended to knowledge graph and behavior graph, the purpose of which was to obtain learners’ overall learning pattern and individual learning pattern.Then, the difference between knowledge structure and behavior structure was used to fit learners’ individual preferences, and recurrent neural network was used to encode and decode learners’ preferences to obtain the distribution of learners’ preference distribution.The experimental results on the real datasets demonstrate that the proposed model has a good effect on predicting learner preferences.

Key words: knowledge graph, behavior graph, GCN, knowledge recommendation

CLC Number:

TN92

Xiaoguang LI, Lei GONG, Xiaoli LI, Xin ZHANG, Ge YU. Learner preferences prediction with mixture embedding of knowledge and behavior graph[J]. Journal on Communications, 2021, 42(8): 130-138.

Figures/Tables 10

方法	AUC
方法	Algebra1/20	Assitment1/10
MF	0.62	0.63
NFM	0.64	0.65
NGCF	0.69	0.70
RippleNet	0.80	0.82
SR-GNN	0.81	0.82
KGBR	$0 . 83$	$0 . 85$
KGBR_{(withoutRGAT)}	$0 . 76$	$0 . 77$
KGBR_{(withoutKGCN)}	$0 . 80$	$0 . 82$

方法	AUC
方法	Algebra1/20	Assitment1/10
KGBR-avg	0.80	0.81
KGBR-inner	0.81	0.81
KGBR-concat	0.82	0.83
KGBR-DEF	$0 . 83$	$0 . 85$

References 26

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数据集	知识点数/个	试题数/个	记录数/个	学生数/个
Algebra1/20	240	1 843	53 427	575
Assitment1/10	113	13 409	46 298	287

数据集	KGCN	RGAT	DEF	编码器	解码器
Algebra 1/20	1×240	1×480	480×1	240×240	240×240
Assitment 1/10	1×113	1×226	226×1	113×113	113×113

Learner preferences prediction with mixture embedding of knowledge and behavior graph

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Figures/Tables 10

References 26

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隐藏层	AUC
隐藏层	Algebra1/20	Assitment1/10
MLP-0	0.63	0.66
MLP-1	0.82	0.83
MLP-2	0.83	0.85
MLP-3	0.81	0.83

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