1 |
KING C, ROBINSON A, VICKERS J. Targeted MOOC captivates students [J]. Nature, 2014, 505(7481): 26-26. 10.1038/505026a
|
2 |
ZHANG J. Can MOOCs be interesting to students? An experimental investigation from regulatory focus perspective[J]. Computers and Education, 2016, 95: 340-351. 10.1016/j.compedu.2016.02.003
|
3 |
董永峰,王雅琮,董瑶,等. 在线学习资源推荐综述[J]. 计算机应用, 2023, 43(6):1655-1663.
|
|
DONG Y F, WANG Y Z, DONG Y, et al. Survey of online learning resource recommendation [J]. Journal of Computer Applications, 2023, 43(6):1655-1663.
|
4 |
PAN L, LI C, LI J, et al. Prerequisite relation learning for concepts in MOOCs [C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA: ACL, 2017: 1447-1456. 10.18653/v1/p17-1133
|
5 |
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. New York: ACM, 2020: 79-88. 10.1145/3397271.3401057
|
6 |
YE B, MAO S, HAO P, et al. Community enhanced course concept recommendation in MOOCs with multiple entities[C]// Proceedings of the 2021 International Conference on Knowledge Science, Engineering and Management, LNCS 12816. Cham: Springer, 2021: 279-293.
|
7 |
PIAO G. Recommending knowledge concepts on MOOC platforms with meta-path-based representation learning [C/OL]// Proceedings of the 14th International Conference on Educational Data Mining [2022-09-19]..
|
8 |
GONG J, WANG C, ZHAO Z, et al. Automatic generation of meta-path graph for concept recommendation in MOOCs [J]. Electronics, 2021, 10(4): No.1671. 10.3390/electronics10141671
|
9 |
GONG J, WAN Y, LIU Y, et al. Reinforced MOOCs concept recommendation in heterogeneous information networks [J]. ACM Transactions on the Web, 2023, 17(3): No.22. 10.1145/3580510
|
10 |
LING Y, SHAN Z. Knowledge concept recommender based on structure enhanced interaction graph neural network[C]// Proceedings of the 2022 International Conference on Knowledge Science, Engineering and Management, LNCS 13368. Cham: Springer, 2022: 173-186.
|
11 |
SHI C, HU B, ZHAO W X, et al. Heterogeneous information network embedding for recommendation [J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 357-370. 10.1109/tkde.2018.2833443
|
12 |
周丽华,王家龙,王丽珍,等. 异质信息网络表征学习综述[J]. 计算机学报, 2022, 45(1):160-189. 10.11897/SP.J.1016.2022.00160
|
|
ZHOU L H, WANG J L, WANG L Z, et al. Heterogeneous information network representation learning: a survey[J]. Chinese Journal of Computers, 2022, 45(1):160-189. 10.11897/SP.J.1016.2022.00160
|
13 |
GORI M, MONFARDINI G, SCARSELLI F. A new model for learning in graph domains [C]// Proceedings of the 2005 IEEE International Joint Conference on Neural Networks — Volume 2. Piscataway: IEEE, 2005:729-734.
|
14 |
MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space [EB/OL]. (2013-09-07) [2022-11-22].. 10.3126/jiee.v3i1.34327
|
15 |
葛尧,陈松灿. 面向推荐系统的图卷积网络[J]. 软件学报, 2020, 31(4):1101-1112.
|
|
GE Y, CHEN S C. Graph convolutional network for recommender systems[J]. Journal of Software, 2020, 31(4):1101-1112.
|
16 |
WANG X, JI H, SHI C, et al. Heterogeneous graph attention network[C]// Proceedings of the 2019 World Wide Web Conference. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2019:2022-2032. 10.1145/3308558.3313562
|
17 |
KANG W C, McAULEY J. Self-attentive sequential recommendation [C]// Proceedings of the 2018 IEEE International Conference on Data Mining. Piscataway: IEEE, 2018: 197-206. 10.1109/icdm.2018.00035
|
18 |
YU J, LUO G, XIAO T, et al. MOOCCube: a large-scale data repository for NLP applications in MOOCs [C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2020:3135-3142. 10.18653/v1/2020.acl-main.285
|
19 |
KRICHENE W, RENDLE S. On sampled metrics for item recommendation[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 1748-1757. 10.1145/3394486.3403226
|
20 |
HE X, HE Z, SONG J, et al. NAIS: neural attentive item similarity model for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(12): 2354-2366. 10.1109/tkde.2018.2831682
|
21 |
KABBUR S, NING X, KARYPIS G. FISM: factored item similarity models for top-n recommender systems [C]// Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2013: 659-667. 10.1145/2487575.2487589
|
22 |
宁懿昕,谢辉,姜火文. 图神经网络社区发现研究综述[J]. 计算机科学, 2021, 48(11A):11-16. 10.11896/jsjkx.210500151
|
|
NING Y X, XIE H, JIANG H W. Survey of graph neural network in community detection[J]. Computer Science, 2021, 48(11A):11-16. 10.11896/jsjkx.210500151
|