Sequence-aware and multi-type behavioral data driven knowledge concept recommendation for massive open online courses

doi:10.3969/j.issn.1000-5641.2024.05.005

Abstract

Abstract:

In massive open online courses (MOOCs), knowledge concept recommendation aims to analyze and extract learning records from a platform to recommend personalized knowledge concepts to users, thereby avoiding the inefficiencies caused by the blind selection of learning content. However, existing methods often lack comprehensive utilization of the multidimensional aspects of user behavior data, such as sequential information and complex interactions. To address this issue, we propose STRec, a sequence-aware and multi-type behavioral data driven knowledge concept recommendation method for MOOCs. STRec extracts the sequential information of knowledge concepts and combines it with the features produced by graph convolutional networks using an attention mechanism. This facilitates the prediction of a user's next knowledge concept of interest. Moreover, by employing multi-type contrastive learning, our method integrates user-interest preferences with various interaction relationships to accurately capture personalized features from complex interactions. The experimental results on the MOOCCube dataset demonstrate that the proposed method outperforms existing baseline models across multiple metrics, validating its effectiveness and practicality in knowledge concept recommendation.

Key words: knowledge concept recommendation, sequence modeling, contrastive learning

CLC Number:

TP391

Junlin REN, Huan WANG, Xiaodi HUANG, Yanting LI, Shenggen JU. Sequence-aware and multi-type behavioral data driven knowledge concept recommendation for massive open online courses[J]. Journal of East China Normal University(Natural Science), 2024, 2024(5): 45-56.

Figures/Tables 7

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关系	元路径
用户–用户	$ {\mathrm{User}}\stackrel{{\mathrm{click}}}{\longrightarrow}{\mathrm{Concept}}\stackrel{{{\mathrm{click}}}^{-1}}{\longrightarrow}{\mathrm{User}} $
	$ {\mathrm{User}}\stackrel{{\mathrm{learn}}}{\longrightarrow}{\mathrm{Course}}\stackrel{{{\mathrm{learn}}}^{-1}}{\longrightarrow}{\mathrm{User}} $
	$ {\mathrm{User}}\stackrel{{\mathrm{watch}}}{\longrightarrow}{\mathrm{Video}}\stackrel{{{\mathrm{watch}}}^{-1}}{\longrightarrow}{\mathrm{User}} $
	$ {\mathrm{User}}\stackrel{{\mathrm{learn}}}{\longrightarrow}{\mathrm{Course}}\stackrel{{{\mathrm{teach}}}^{-1}}{\longrightarrow}{\mathrm{Teacher}}\stackrel{{\mathrm{teach}}}{\longrightarrow}{\mathrm{Course}}\stackrel{{{\mathrm{learn}}}^{-1}}{\longrightarrow}{\mathrm{User}} $
知识概念–知识概念	$ {\mathrm{Concept}}\stackrel{{{\mathrm{click}}}^{-1}}{\longrightarrow}{\mathrm{User}}\stackrel{{\mathrm{click}}}{\longrightarrow}{\mathrm{Concept}} $

实体	数量	关系	交互总数
用户	1600	用户–知识概念	385707
知识概念	1645	用户–课程	20896
课程	706	用户–课程–视频	79148
视频	1116	用户–课程–老师	34331
老师	1737	课程–知识概念	51022

Model	HR@5	HR@10	NDCG@5	NDCG@10	MRR
ACKRec	0.8131	0.8737	0.6875	0.7070	0.6537
w-UK	0.8225	0.8862	0.7041	0.7251	0.6656
w-UC	0.8343	0.8887	0.7345	0.7522	0.7339
w-UV	0.8015	0.8712	0.6876	0.7103	0.7541
w-UT	0.8162	0.8706	0.7042	0.7221	0.7134
STRec	0.8524	0.9368	0.7469	0.7747	0.7590

Model	HR@5	HR@10	NDCG@5	NDCG@10	MRR
STRec	0.8524	0.9368	0.7469	0.7747	0.7590
w/o SA	0.8275	0.8831	0.7210	0.7393	0.7477
w/o MutilCL	0.8212	0.8868	0.7169	0.7385	0.6878

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