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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References 28

1	GONG J, WANG S, WANG J, et al. Attentional graph convolutional networks for knowledge concept recommendation in MOOCs in a heterogeneous view [C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2020: 79-88.
2	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [EB/OL]. (2017-06-12)[2024-06-23]. https://arxiv.org/abs/1706.03762.
3	PIAO G Y. Recommending knowledge concepts on MOOC platforms with meta-path-based representation learning [C/OL]. International Educational Data Mining Society. (2021-06-21)[2024-05-27]. https://eric.ed.gov/?id=ED615611.
4	WANG X, MA W, GUO L, et al. HGNN: Hyperedge-based graph neural network for MOOC course recommendation [J]. Information Processing & Management, 2022, 59(3): 102938.
5	ZHANG H, SHEN X, YI B, et al. KGAN: Knowledge grouping aggregation network for course recommendation in MOOCs [J]. Expert Systems with Applications, 2023, 211: 118344.
6	HAO P, LI Y, BAI C. Meta-relationship for course recommendation in MOOCs [J]. Multimedia Systems, 2023, 29(1): 235-246.
7	GU H, DUAN Z, XIE P, et al. Modeling balanced explicit and implicit Relations with contrastive learning for knowledge concept recommendation in MOOCs [C]// Proceedings of the ACM on Web Conference 2024. ACM, 2024: 3712-3722.
8	ZHANG J, HAO B, CHEN B, et al. Hierarchical reinforcement learning for course recommendation in MOOCs [C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33(1): 435-442.
	ZHANG J, HAO B, CHEN B, et al. Hierarchical reinforcement learning for course recommendation in MOOCs [C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33(1): 435-442.
9	GONG J, WANG C, ZHAO Z, et al. Automatic generation of meta-path graph for concept recommendation in MOOCs [J]. Electronics, 2021, 10(14): 1671.
10	LIANG Z, MU L, CHEN J, et al. Graph path fusion and reinforcement reasoning for recommendation in MOOCs [J]. Education and Information Technologies, 2023, 28(1): 525-545.
11	HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks [EB/OL]. (2016-03-29)[2024-06-23]. http://arxiv.org/abs/1511.06939.
12	QUADRANA M, KARATZOGLOU A, HIDASI B, et al. Personalizing session-based recommendations with hierarchical recurrent neural networks [C]// Proceedings of the Eleventh ACM Conference on Recommender Systems. New York: ACM, 2017: 130-137.
13	WU C Y, AHMED A, BEUTEL A, et al. Recurrent recommender networks [C]// Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. Cambridge: ACM, 2017: 495-503.
14	DUAN J, ZHANG P F, QIU R, et al. Long short-term enhanced memory for sequential recommendation [J]. World Wide Web, 2023, 26(2): 561-583.
15	KANG W C, MCAULEY J. Self-attentive sequential recommendation [C]// 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018: 197-206.
16	LI J, WANG Y, MCAULEY J. Time interval aware self-attention for sequential recommendation [C]// Proceedings of the 13th International Conference on Web Search and Data Mining. ACM, 2020: 322-330.
17	SUN F, LIU J, WU J, et al. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer [C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM, 2019: 1441-1450.
18	ZHOU K, WANG H, ZHAO W X, et al. S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization [C]// Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York: ACM, 2020: 1893-1902.
19	CHEN T, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations [C]// Proceedings of the 37th International Conference on Machine Learning. PMLR, 2020: 1597-1607.
20	GAO T, YAO X, CHEN D. SimCSE: Simple contrastive learning of sentence embeddings [C]// MOENS M F, HUANG X, SPECIA L, et al. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2021: 6894-6910.
21	LIU Z, CHEN Y, LI J, et al. Contrastive self-supervised sequential recommendation with robust augmentation [EB/OL]. (2021-08-14)[2024-05-28]. http://arxiv.org/abs/2108.06479.
22	DANG Y, YANG E, GUO G, et al. Uniform sequence better: Time interval aware data augmentation for sequential recommendation [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(4): 4225-4232.
23	XIE X, SUN F, LIU Z, et al. Contrastive learning for sequential recommendation [C]// 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022: 1259-1273.
24	CHEN M, HUANG C, XIA L, et al. Heterogeneous graph contrastive learning for recommendation [C]// Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. ACM, 2023: 544-552.
25	SHUAI J, ZHANG K, WU L, et al. A review-aware graph contrastive learning framework for recommendation [C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022: 1283-1293.
26	YU J, YIN H, XIA X, et al. Are graph augmentations necessary? Simple graph contrastive learning for recommendation [C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022: 1294-1303.
27	MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space [EB/OL]. (2013-09-07)[2024-05-30]. http://arxiv.org/abs/1301.3781.
28	NAIR V, HINTON G E. Rectified linear units improve restricted boltzmann machines [C]// Proceedings of the 27th International Conference on Machine Learning. 2010: 807-814.

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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