Hengnian Gu
ABSTRACT
The knowledge concept recommendation in Massive Open Online Courses (MOOCs) is a significant issue that has garnered widespread attention. Existing methods primarily rely on the explicit relations between users and knowledge concepts on the MOOC platforms for recommendation. However, there are numerous implicit relations (e.g., shared interests or same knowledge levels between users) generated within the users' learning activities on the MOOC platforms. Existing methods fail to consider these implicit relations, and these relations themselves are difficult to learn and represent, causing poor performance in knowledge concept recommendation and an inability to meet users' personalized needs. To address this issue, we propose a novel framework based on contrastive learning, which can represent and balance the explicit and implicit relations for knowledge concept recommendation in MOOCs (CL-KCRec). Specifically, we first construct a MOOCs heterogeneous information network (HIN) by modeling the data from the MOOC platforms. Then, we utilize a relation-updated graph convolutional network and stacked multi-channel graph neural network to represent the explicit and implicit relations in the HIN, respectively. Considering that the quantity of explicit relations is relatively fewer compared to implicit relations in MOOCs, we propose a contrastive learning with prototypical graph to enhance the representations of both relations to capture their fruitful inherent relational knowledge, which can guide the propagation of students' preferences within the HIN. Based on these enhanced representations, to ensure the balanced contribution of both towards the final recommendation, we propose a dual-head attention mechanism for balanced fusion. Experimental results demonstrate that CL-KCRec outperforms several state-of-the-art baselines on real-world datasets in terms of HR, NDCG and MRR.
Supplemental Material
- Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, and Ronghua Luo. 2023. Heterogeneous graph contrastive learning for recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (Singapore, Singapore) (WSDM '23). Association for Computing Machinery, New York, NY, USA, 544--552. https://doi.org/10.1145/3539597.3570484Google Scholar
Digital Library
- Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, and Jiashi Feng. 2018. A^ 2-nets: Double attention networks. Advances in neural information processing systems , Vol. 31 (2018), 350--359. https://proceedings.neurips.cc/paper/2018/hash/e165421110ba03099a1c0393373c5b43-Abstract.htmlGoogle Scholar
- Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. 2020. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (Seattle, WA, USA) (CVPR '20). IEEE, New York, NY, USA, 5154--5163. https://doi.org/10.1109/CVPR42600.2020.00520Google ScholarCross Ref
- Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (Halifax, NS, Canada) (KDD '17). ACM, New York, NY, USA, 135--144. https://doi.org/10.1145/3097983.3098036Google ScholarDigital Library
- Hao Fu, Shaojun Zhou, Qihong Yang, Junjie Tang, Guiquan Liu, Kaikui Liu, and Xiaolong Li. 2021. LRC-BERT: latent-representation contrastive knowledge distillation for natural language understanding. In Proceedings of the AAAI Conference on Artificial Intelligence (Virtual Event) (AAAI '21, Vol. 35). AAAI, Menlo Park, CA, USA, 12830--12838. https://doi.org/10.1609/AAAI.V35I14.17518Google ScholarCross Ref
- Tao-yang Fu, Wang-Chien Lee, and Zhen Lei. 2017. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (Singapore, Singapore) (CIKM '17). ACM, New York, NY, USA, 1797--1806. https://doi.org/10.1145/3132847.3132953Google ScholarDigital Library
- Jibing Gong, Yao Wan, Ye Liu, Xuewen Li, Yi Zhao, Cheng Wang, Yuting Lin, Xiaohan Fang, Wenzheng Feng, Jingyi Zhang, et al. 2023. Reinforced moocs concept recommendation in heterogeneous information networks. ACM Transactions on the Web , Vol. 17, 3 (2023), 1--27. https://doi.org/10.1145/3580510Google ScholarDigital Library
- Jibing Gong, Shen Wang, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, and Philip S Yu. 2020. Attentional graph convolutional networks for knowledge concept recommendation in moocs in a heterogeneous view. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (Virtual Event, China) (SIGIR '20). ACM, New York, NY, USA, 79--88. https://doi.org/10.1145/3397271.3401057Google ScholarDigital Library
- Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. NAIS: Neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 30, 12 (2018), 2354--2366. https://doi.org/10.1109/TKDE.2018.2831682Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (Perth, Australia) (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173--182. https://doi.org/10.1145/3038912.3052569Google ScholarDigital Library
- Lu Jiang, Kunpeng Liu, Yibin Wang, Dongjie Wang, Pengyang Wang, Yanjie Fu, and Minghao Yin. 2023. Reinforced Explainable Knowledge Concept Recommendation in MOOCs. ACM Transactions on Intelligent Systems and Technology, Vol. 14, 3 (2023), 1--20. https://doi.org/10.1145/3579991Google ScholarDigital Library
- Santosh Kabbur, Xia Ning, and George Karypis. 2013. Fism: factored item similarity models for top-n recommender systems. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (Chicago, Illinois, USA) (KDD '13). ACM, New York, NY, USA, 659--667. https://doi.org/10.1145/2487575.2487589Google ScholarDigital Library
- Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT '19). ACL, Stroudsburg, PA, USA, 4171--4186. https://doi.org/10.18653/V1/N19--1423Google ScholarCross Ref
- Junnan Li, Pan Zhou, Caiming Xiong, and Steven Hoi. 2021. Prototypical Contrastive Learning of Unsupervised Representations. In International Conference on Learning Representations (Virtual Event, Austria) (ICLR '21). OpenReview.net, Online, bibinfonumpages9 pages. https://openreview.net/forum?id=KmykpuSrjcqGoogle Scholar
- Weimin Li, Lin Ni, Jianjia Wang, and Can Wang. 2022. Collaborative representation learning for nodes and relations via heterogeneous graph neural network. Knowledge-Based Systems , Vol. 255 (2022), 109673. https://doi.org/10.1016/j.knosys.2022.109673Google ScholarDigital Library
- Bin Liang, Qinlin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, and Ruifeng Xu. 2022. Jointcl: a joint contrastive learning framework for zero-shot stance detection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL '22, Vol. 1). ACL, Dublin, Ireland, 81--91. https://doi.org/10.18653/v1/2022.acl-long.7Google ScholarCross Ref
- Zibo Liang, Lan Mu, Jie Chen, and Qing Xie. 2023. Graph path fusion and reinforcement reasoning for recommendation in MOOCs. Education and Information Technologies , Vol. 28, 1 (2023), 525--545. https://doi.org/10.1007/s10639-022--11178--2Google ScholarDigital Library
- Shuai Lin, Chen Liu, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, and Xiaodan Liang. 2022. Prototypical graph contrastive learning. IEEE Transactions on Neural Networks and Learning Systems , Vol. 35 (2022), 2747--2758. https://doi.org/10.1109/TNNLS.2022.3191086Google ScholarCross Ref
- Xiaoling Long, Chao Huang, Yong Xu, Huance Xu, Peng Dai, Lianghao Xia, and Liefeng Bo. 2021. Social recommendation with self-supervised metagraph informax network. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (Virtual Event, Queensland, Australia) (CIKM '21). ACM, New York, NY, USA, 1160--1169. https://doi.org/10.1145/3459637.3482480Google ScholarDigital Library
- Luis Miguel Lázaro Lorente, Ana Ancheta Arrabal, and Cristina Pulido-Montes. 2020. The right to education and ict during covid-19: An international perspective. Sustainability, Vol. 12, 21 (2020), 9091. https://doi.org/10.3390/su12219091Google ScholarCross Ref
- Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio. 2016. Holographic embeddings of knowledge graphs. In Proceedings of the AAAI conference on artificial intelligence (Phoenix, Arizona, USA) (AAAI '16). AAAI, Menlo Park, CA, USA, 1955--1961. https://doi.org/10.1609/AAAI.V30I1.10314Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal, QC, Canada) (UAI '09). AUAI, Online, 452--461. https://www.auai.org/uai2009/papers/UAI2009_0139_48141db02b9f0b02bc7158819ebfa2c7.pdfGoogle Scholar
- J Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative filtering recommender systems. In The adaptive web: methods and strategies of web personalization. Lecture Notes in Computer Science, Vol. 4321. Springer, Berlin, Germany, 291--324. https://doi.org/10.1007/978--3--540--72079--9_9Google ScholarCross Ref
- Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference (ISWC '18, Vol. 10843). Springer, Berlin, Germany, 593--607. https://doi.org/10.1007/978--3--319--93417--4_38Google ScholarDigital Library
- Chuan Shi, Binbin Hu, Wayne Xin Zhao, and S Yu Philip. 2018. Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 31, 2 (2018), 357--370. https://doi.org/10.1109/TKDE.2018.2833443Google ScholarDigital Library
- Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2020. Composition-based Multi-Relational Graph Convolutional Networks. In 8th International Conference on Learning Representations (ICLR '20). OpenReview.net, Online, bibinfonumpages8 pages. https://openreview.net/forum?id=BylA_C4tPrGoogle Scholar
- Petar Velivc ković , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations (Vancouver, Canada) (ICLR '18). OpenReview.net, Online, bibinfonumpages9 pages. https://openreview.net/forum?id=rJXMpikCZGoogle Scholar
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval (Paris, France) (SIGIR'19). ACM, New York, NY, USA, 165--174. https://doi.org/10.1145/3331184.3331267Google ScholarDigital Library
- Xinhua Wang, Linzhao Jia, Lei Guo, and Fangai Liu. 2023. Multi-aspect heterogeneous information network for MOOC knowledge concept recommendation. Applied Intelligence, Vol. 53, 10 (2023), 11951--11965. https://doi.org/10.1007/s10489-022-04025-xGoogle ScholarDigital Library
- Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, and Dawei Yin. 2022. Contrastive meta learning with behavior multiplicity for recommendation. In Proceedings of the fifteenth ACM international conference on web search and data mining (Virtual Event, AZ, USA) (WSDM '22). ACM, New York, NY, USA, 1120--1128. https://doi.org/10.1145/3488560.3498527Google ScholarDigital Library
- Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (Virtual Event, Canada,) (SIGIR '21). ACM, New York, NY, USA, 726--735. https://doi.org/10.1145/3404835.3462862Google ScholarDigital Library
- Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li. 2022. Knowledge graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR '22). ACM, New York, NY, USA, 1434--1443. https://doi.org/10.1145/3477495.3532009Google ScholarDigital Library
- Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, et al. 2020. MOOCCube: a large-scale data repository for NLP applications in MOOCs. In Proceedings of the 58th annual meeting of the association for computational linguistics (ACL '20). ACL, Stroudsburg,PA,USA, 3135--3142. https://doi.org/10.18653/v1/2020.acl-main.285Google ScholarCross Ref
- Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang. 2021. Self-supervised multi-channel hypergraph convolutional network for social recommendation. In Proceedings of the web conference 2021 (Ljubljana, Slovenia) (WWW '21). ACM, New York, NY, USA, 413--424. https://doi.org/10.1145/3442381.3449844Google ScholarDigital Library
- Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2019. Graph transformer networks. Advances in neural information processing systems , Vol. 32 (2019), 11960--11970. https://proceedings.neurips.cc/paper/2019/hash/9d63484abb477c97640154d40595a3bb-Abstract.htmlGoogle Scholar
- Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, S Yi Sean, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2022. Graph Transformer Networks: Learning meta-path graphs to improve GNNs. Neural Networks , Vol. 153 (2022), 104--119. https://doi.org/10.1016/j.neunet.2022.05.026Google ScholarDigital Library
- Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (Anchorage, AK, USA) (KDD '19). ACM, New York, NY, USA, 793--803. https://doi.org/10.1145/3292500.3330961Google ScholarDigital Library
- Han Zhang, Maosong Sun, Xiaochen Wang, Zhengyang Song, Jie Tang, and Jimeng Sun. 2017. Smart jump: Automated navigation suggestion for videos in moocs. In Proceedings of the 26th international conference on world wide web companion (Perth, Australia) (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 331--339. https://doi.org/10.1145/3041021.3054166Google ScholarDigital Library
Index Terms
- Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs
Recommendations
Reinforced Explainable Knowledge Concept Recommendation in MOOCs
In this article, we study knowledge concept recommendation in Massive Open Online Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units (e.g., videos) in MOOCs, refer to topics and skills that students are expected to ...
Modeling User Exposure with Explicit and Implicit Social Relations for Recommendation
ICFET '19: Proceedings of the 5th International Conference on Frontiers of Educational TechnologiesSocial recommender systems have been well studied in both academia and industry. Social information helps to solve the data sparsity and cold start problems in traditional recommender systems, while most existing works in social recommendation assume ...
UGRec: Modeling Directed and Undirected Relations for Recommendation
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information RetrievalThe recommender systems, which merely leverage user-item interactions for user preference prediction (such as the collaborative filtering-based ones), often face dramatic performance degradation when the interactions of users or items are insufficient. ...
Comments