Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis
In recent years, online education has seen remarkable growth, particularly through Massive Open Online Course (MOOC) platforms. These platforms offer a wide range of open courses, making it a problem for users to select one from thousands of courses. Navigating this vast selection led to recommendat...
- Autores:
-
Pardo Bravo, Santiago
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/73627
- Acceso en línea:
- https://hdl.handle.net/1992/73627
- Palabra clave:
- Course recommender systems
Graph neuronal networks
Massive Open Online Course
Ingeniería
- Rights
- embargoedAccess
- License
- Attribution 4.0 International
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Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis |
| title |
Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis |
| spellingShingle |
Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis Course recommender systems Graph neuronal networks Massive Open Online Course Ingeniería |
| title_short |
Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis |
| title_full |
Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis |
| title_fullStr |
Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis |
| title_full_unstemmed |
Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis |
| title_sort |
Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis |
| dc.creator.fl_str_mv |
Pardo Bravo, Santiago |
| dc.contributor.advisor.none.fl_str_mv |
Manrique Piramanrique, Rubén Francisco |
| dc.contributor.author.none.fl_str_mv |
Pardo Bravo, Santiago |
| dc.contributor.jury.none.fl_str_mv |
Manrique Piramanrique, Rubén Francisco |
| dc.subject.keyword.eng.fl_str_mv |
Course recommender systems Graph neuronal networks Massive Open Online Course |
| topic |
Course recommender systems Graph neuronal networks Massive Open Online Course Ingeniería |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
In recent years, online education has seen remarkable growth, particularly through Massive Open Online Course (MOOC) platforms. These platforms offer a wide range of open courses, making it a problem for users to select one from thousands of courses. Navigating this vast selection led to recommendation systems emerging, enhancing the learning experience by guiding students toward courses aligned with their interests. Addressing this challenge involves three primary approaches. The content-based approach uses the characteristics content of the seen courses to suggest new ones. Collaborative filtering taps into interactions among users with similar preferences, proposing relevant courses. The hybrid approach combines both methods for a comprehensive recommendation system. In a context marked by the rapid expansion of Massive Open Online Courses (MOOCs), there has been a parallel surge in the field of deep learning along with its diverse architectures. Against this backdrop, the focus of this investigation centers on Graph Neural Networks (GNNs), a deep learning architecture explicitly tailored to tackle structured data. In this study, we propose and validate a novel course recommendation model utilizing GNNs on the Xuetang MOOC platform. This research's significance lies in enhancing online course recommendations using GNNs, aiming to elevate user satisfaction and learning effectiveness. Finally, for a comprehensive analysis, we'll compare our model against traditional approaches using the same dataset. This determines GNN's convenience in recommendation precision and relevance against the traditional methods. |
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2023 |
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2023-12-04 |
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2024-01-30T19:30:15Z |
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2024-01-27 |
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2025-01-24 |
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Trabajo de grado - Pregrado |
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eng |
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Class Central. A Decade of MOOCs: A Review of MOOC Stats and Trendsin 2021. 2021. url:https://www.classcentral.com/report/moocstats-2021/. Shrooq Algarni and Frederick Sheldon. “Systematic Review of Recommendation Systems for Course Selection”. In: Machine Learning and Knowledge Extraction 5.2 (2023), pp. 560–596. issn: 2504-4990. doi: 10.3390/make5020033. url: https://www.mdpi.com/2504-4990/5/2/33. Mayur Badole. A comprehensive guide on recommendation engines and Implementation. Apr. 2022. url: https://www.analyticsvidhya.com/blog/2022/03/a-comprehensive-guide-on-recommendation-enginesand-implementation/. Tieyuan Liu et al. “A Review of Deep Learning-Based Recommender System in e-Learning Environments”. In: Artif. Intell. Rev. 55.8 (Dec. 2022),pp. 5953–5980. issn: 0269-2821. doi: 10 . 1007 / s10462 - 022 - 10135 - 2.url: https://doi.org/10.1007/s10462-022-10135-2. Xiang Wang et al. “Neural Graph Collaborative Filtering”. In: Proceedings of the 42nd International ACM SIGIR Conference on Research andDevelopment in Information Retrieval. ACM, July 2019. doi: 10.1145/3331184 . 3331267. url: https : / / doi . org / 10 . 1145 % 2F3331184 .3331267. Xiangnan He et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. 2020. arXiv: 2002.02126 [cs.IR]. JIANG Huowen WU Jing XIE Hui. “Survey of Graph Neural Network in Recommendation System”. In: Journal of Frontiers of Computer Science Technology 16.10, 2249 (2022), pp. 2249–2263. doi: http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.2203004. Jifan Yu et al. “MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs”. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics, July 2020, pp. 3135–3142. doi: 10.18653/v1/2020.acl-main.285. url: https://aclanthology.org/2020.acl-main.285. Huanyu Zhang et al. “KGAN: Knowledge Grouping Aggregation Network for course recommendation in MOOCs”. In: Expert Systems with Applications 211 (2023), p. 118344. issn: 0957-4174. doi: https://doi.org/10.1016/j.eswa.2022.118344. url: https://www.sciencedirect.com/science/article/pii/S0957417422014646. Bifeng Li et al. “A personalized recommendation framework based on MOOC system integrating deep learning and big data”. In: Computers and Electrical Engineering 106 (2023), p. 108571. issn: 0045-7906. doi: https://doi.org/10.1016/j.compeleceng.2022.108571. url: https://www.sciencedirect.com/science/article/pii/S0045790622007868. Shengjun Yin, Kailai Yang, and Hongzhi Wang. “A MOOC Courses Recommendation System Based on Learning Behaviours”. In: Proceedings of the ACM Turing Celebration Conference - China. ACM TURC ’20. Hefei, China: Association for Computing Machinery, 2020, pp. 133–137. isbn:9781450375344. doi: 10.1145/3393527.3393550. url: https://doi.org/10.1145/3393527.3393550. Yusfi Adilaksa and Aina Musdholifah. “Recommendation System for Elective Courses using Content-based Filtering and Weighted Cosine Similarity”. In: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). 2021, pp. 51–55. doi: 10.1109/ISRITI54043.2021.9702788. Shivam Baldha. Introduction to collaborative filtering. Mar. 2022. url:https://www.analyticsvidhya.com/blog/2022/02/introductionto-collaborative-filtering/. Lili Wu. “Collaborative Filtering Recommendation Algorithm for MOOC Resources Based on Deep Learning”. In: Complexity 2021 (Apr. 2021),pp. 1–11. doi: 10.1155/2021/5555226. Mehbooba P. Shareef, Linda Rose Jimson, and Babita R. Jose. “Hybrid Explainable Educational Recommender Using Self-attention and KnowledgeBased Systems for E-Learning in MOOC Platforms”. In: Responsible Data Science. Ed. by Jimson Mathew et al. Singapore: Springer Nature Singapore, 2022, pp. 61–74. isbn: 978-981-19-4453-6. Xin Zhou et al. Layer-refined Graph Convolutional Networks for Recommendation. 2022. arXiv: 2207.11088 [cs.IR]. Xinhua Wang et al. “HGNN: Hyperedge-Based Graph Neural Network for MOOC Course Recommendation”. In: Inf. Process. Manage. 59.3 (May 2022). issn: 0306-4573. doi: 10.1016/j.ipm.2022.102938. url: https://doi.org/10.1016/j.ipm.2022.102938. Mengyue Hang et al. “Lightweight Compositional Embeddings for Incremental Streaming Recommendation”. In: arXiv e-prints, arXiv:2202.02427(Feb. 2022), arXiv:2202.02427. doi: 10.48550/arXiv.2202.02427. arXiv:2202.02427 [cs.LG]. Jiawei Chen et al. “CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation”. In: arXiv e-prints, arXiv:2011.07739 (Nov. 2020),arXiv:2011.07739. doi: 10.48550/arXiv.2011.07739. arXiv: 2011.07739[cs.IR]. Shuvayan Das. Beginners Guide to content based Recommender Systems.Aug. 2023. url: https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/#:~:text=Content%2Dbased%20recommender%20systems%20are, mapping%20it%20to%20users%E2%80%99%20preferences. Jacob Devlin et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 2019. arXiv: 1810.04805 [cs.CL]. Jie Lu et al. “VMD and self-attention mechanism-based Bi-LSTM model for fault detection of optical fiber composite submarine cables”. In: EURASIP Journal on Applied Signal Processing 2023.1, 29 (Dec. 2023), p. 29. doi: 10.1186/s13634-023-00988-2. Henry W. Leung and Jo Bovy. “Towards an astronomical foundation model for stars with a transformer-based model”. In: 527.1 (Jan. 2024), pp. 1494–1520. doi:10.1093/mnras/stad3015. arXiv: 2308.10944 [astro-ph.IM]. Jie Zhou et al. Graph Neural Networks: A Review of Methods and Applications. 2021. arXiv: 1812.08434 [cs.LG]. Rick Merritt. ¿Qu´e son las redes neuronales de Gr´aficos?: Blog De Nvidia. Oct. 2022. url: https://la.blogs.nvidia.com/2022/10/31/que-sonlas-graph-neural-networks/. Eric W Weisstein. Bipartite Graph. url: https://mathworld.wolfram.com/BipartiteGraph.html. Thomas N. Kipf and Max Welling. Semi-Supervised Classification withGraph Convolutional Networks. 2017. arXiv: 1609.02907 [cs.LG]. |
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Manrique Piramanrique, Rubén FranciscoPardo Bravo, SantiagoManrique Piramanrique, Rubén Francisco2024-01-30T19:30:15Z2025-01-242023-12-042024-01-27https://hdl.handle.net/1992/73627instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/In recent years, online education has seen remarkable growth, particularly through Massive Open Online Course (MOOC) platforms. These platforms offer a wide range of open courses, making it a problem for users to select one from thousands of courses. Navigating this vast selection led to recommendation systems emerging, enhancing the learning experience by guiding students toward courses aligned with their interests. Addressing this challenge involves three primary approaches. The content-based approach uses the characteristics content of the seen courses to suggest new ones. Collaborative filtering taps into interactions among users with similar preferences, proposing relevant courses. The hybrid approach combines both methods for a comprehensive recommendation system. In a context marked by the rapid expansion of Massive Open Online Courses (MOOCs), there has been a parallel surge in the field of deep learning along with its diverse architectures. Against this backdrop, the focus of this investigation centers on Graph Neural Networks (GNNs), a deep learning architecture explicitly tailored to tackle structured data. In this study, we propose and validate a novel course recommendation model utilizing GNNs on the Xuetang MOOC platform. This research's significance lies in enhancing online course recommendations using GNNs, aiming to elevate user satisfaction and learning effectiveness. Finally, for a comprehensive analysis, we'll compare our model against traditional approaches using the same dataset. This determines GNN's convenience in recommendation precision and relevance against the traditional methods.Ingeniero de Sistemas y ComputaciónPregrado34 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfGraph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysisTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPCourse recommender systemsGraph neuronal networksMassive Open Online CourseIngenieríaClass Central. A Decade of MOOCs: A Review of MOOC Stats and Trendsin 2021. 2021. url:https://www.classcentral.com/report/moocstats-2021/.Shrooq Algarni and Frederick Sheldon. “Systematic Review of Recommendation Systems for Course Selection”. In: Machine Learning and Knowledge Extraction 5.2 (2023), pp. 560–596. issn: 2504-4990. doi: 10.3390/make5020033. url: https://www.mdpi.com/2504-4990/5/2/33.Mayur Badole. A comprehensive guide on recommendation engines and Implementation. Apr. 2022. url: https://www.analyticsvidhya.com/blog/2022/03/a-comprehensive-guide-on-recommendation-enginesand-implementation/.Tieyuan Liu et al. “A Review of Deep Learning-Based Recommender System in e-Learning Environments”. In: Artif. Intell. Rev. 55.8 (Dec. 2022),pp. 5953–5980. issn: 0269-2821. doi: 10 . 1007 / s10462 - 022 - 10135 - 2.url: https://doi.org/10.1007/s10462-022-10135-2.Xiang Wang et al. “Neural Graph Collaborative Filtering”. In: Proceedings of the 42nd International ACM SIGIR Conference on Research andDevelopment in Information Retrieval. ACM, July 2019. doi: 10.1145/3331184 . 3331267. url: https : / / doi . org / 10 . 1145 % 2F3331184 .3331267.Xiangnan He et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. 2020. arXiv: 2002.02126 [cs.IR].JIANG Huowen WU Jing XIE Hui. “Survey of Graph Neural Network in Recommendation System”. In: Journal of Frontiers of Computer Science Technology 16.10, 2249 (2022), pp. 2249–2263. doi: http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.2203004.Jifan Yu et al. “MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs”. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics, July 2020, pp. 3135–3142. doi: 10.18653/v1/2020.acl-main.285. url: https://aclanthology.org/2020.acl-main.285.Huanyu Zhang et al. “KGAN: Knowledge Grouping Aggregation Network for course recommendation in MOOCs”. In: Expert Systems with Applications 211 (2023), p. 118344. issn: 0957-4174. doi: https://doi.org/10.1016/j.eswa.2022.118344. url: https://www.sciencedirect.com/science/article/pii/S0957417422014646.Bifeng Li et al. “A personalized recommendation framework based on MOOC system integrating deep learning and big data”. In: Computers and Electrical Engineering 106 (2023), p. 108571. issn: 0045-7906. doi: https://doi.org/10.1016/j.compeleceng.2022.108571. url: https://www.sciencedirect.com/science/article/pii/S0045790622007868.Shengjun Yin, Kailai Yang, and Hongzhi Wang. “A MOOC Courses Recommendation System Based on Learning Behaviours”. In: Proceedings of the ACM Turing Celebration Conference - China. ACM TURC ’20. Hefei, China: Association for Computing Machinery, 2020, pp. 133–137. isbn:9781450375344. doi: 10.1145/3393527.3393550. url: https://doi.org/10.1145/3393527.3393550.Yusfi Adilaksa and Aina Musdholifah. “Recommendation System for Elective Courses using Content-based Filtering and Weighted Cosine Similarity”. In: 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). 2021, pp. 51–55. doi: 10.1109/ISRITI54043.2021.9702788.Shivam Baldha. Introduction to collaborative filtering. Mar. 2022. url:https://www.analyticsvidhya.com/blog/2022/02/introductionto-collaborative-filtering/.Lili Wu. “Collaborative Filtering Recommendation Algorithm for MOOC Resources Based on Deep Learning”. In: Complexity 2021 (Apr. 2021),pp. 1–11. doi: 10.1155/2021/5555226.Mehbooba P. Shareef, Linda Rose Jimson, and Babita R. Jose. “Hybrid Explainable Educational Recommender Using Self-attention and KnowledgeBased Systems for E-Learning in MOOC Platforms”. In: Responsible Data Science. Ed. by Jimson Mathew et al. Singapore: Springer Nature Singapore, 2022, pp. 61–74. isbn: 978-981-19-4453-6.Xin Zhou et al. Layer-refined Graph Convolutional Networks for Recommendation. 2022. arXiv: 2207.11088 [cs.IR].Xinhua Wang et al. “HGNN: Hyperedge-Based Graph Neural Network for MOOC Course Recommendation”. In: Inf. Process. Manage. 59.3 (May 2022). issn: 0306-4573. doi: 10.1016/j.ipm.2022.102938. url: https://doi.org/10.1016/j.ipm.2022.102938.Mengyue Hang et al. “Lightweight Compositional Embeddings for Incremental Streaming Recommendation”. In: arXiv e-prints, arXiv:2202.02427(Feb. 2022), arXiv:2202.02427. doi: 10.48550/arXiv.2202.02427. arXiv:2202.02427 [cs.LG].Jiawei Chen et al. “CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation”. In: arXiv e-prints, arXiv:2011.07739 (Nov. 2020),arXiv:2011.07739. doi: 10.48550/arXiv.2011.07739. arXiv: 2011.07739[cs.IR].Shuvayan Das. Beginners Guide to content based Recommender Systems.Aug. 2023. url: https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/#:~:text=Content%2Dbased%20recommender%20systems%20are, mapping%20it%20to%20users%E2%80%99%20preferences.Jacob Devlin et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 2019. arXiv: 1810.04805 [cs.CL].Jie Lu et al. “VMD and self-attention mechanism-based Bi-LSTM model for fault detection of optical fiber composite submarine cables”. In: EURASIP Journal on Applied Signal Processing 2023.1, 29 (Dec. 2023), p. 29. doi: 10.1186/s13634-023-00988-2.Henry W. Leung and Jo Bovy. “Towards an astronomical foundation model for stars with a transformer-based model”. In: 527.1 (Jan. 2024), pp. 1494–1520. doi:10.1093/mnras/stad3015. arXiv: 2308.10944 [astro-ph.IM].Jie Zhou et al. Graph Neural Networks: A Review of Methods and Applications. 2021. arXiv: 1812.08434 [cs.LG].Rick Merritt. ¿Qu´e son las redes neuronales de Gr´aficos?: Blog De Nvidia. Oct. 2022. url: https://la.blogs.nvidia.com/2022/10/31/que-sonlas-graph-neural-networks/.Eric W Weisstein. Bipartite Graph. url: https://mathworld.wolfram.com/BipartiteGraph.html.Thomas N. Kipf and Max Welling. 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