Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
The main reason students drop out of online courses is often that they lose interest during learning. Moreover, it is not easy for students to choose an appropriate course before actually learning it. Course recommendation is necessary to address this problem. Most existing course recommendation met...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/4/2404 |
| _version_ | 1827758818384150528 |
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| author | Zhengyang Wu Qingyu Liang Zehui Zhan |
| author_facet | Zhengyang Wu Qingyu Liang Zehui Zhan |
| author_sort | Zhengyang Wu |
| collection | DOAJ |
| description | The main reason students drop out of online courses is often that they lose interest during learning. Moreover, it is not easy for students to choose an appropriate course before actually learning it. Course recommendation is necessary to address this problem. Most existing course recommendation methods depend on the interaction result (e.g., completion rate, grades, etc.). However, the long period required to complete a course, especially large-scale online courses in higher education, can lead to serious sparsity of interaction results. In view of this, we propose a novel course recommendation method named HGE-CRec, which utilizes context formation for heterogeneous graphs to model students and courses. HGE-CRec develops meta-path embedding simulation and meta-path weight fusion to enhance the meta-path embedding set, which can expand the learning space of the prediction model and improve the representation ability of meta-path embedding, thereby avoiding tedious manual setting of the meta-path and improving the effectiveness of the resulting recommendations. Extensive experiments show that the proposed approach has advantages over a number of existing baseline methods. |
| first_indexed | 2024-03-11T09:11:52Z |
| format | Article |
| id | doaj.art-43035c33d9ea4cec943129ea1447bdb7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:11:52Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-43035c33d9ea4cec943129ea1447bdb72023-11-16T18:55:19ZengMDPI AGApplied Sciences2076-34172023-02-01134240410.3390/app13042404Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous GraphZhengyang Wu0Qingyu Liang1Zehui Zhan2School of Computer Science, South China Normal University, Guangzhou 510631, ChinaSchool of Computer Science, South China Normal University, Guangzhou 510631, ChinaSchool of Information Technology in Education, South China Normal University, Guangzhou 510631, ChinaThe main reason students drop out of online courses is often that they lose interest during learning. Moreover, it is not easy for students to choose an appropriate course before actually learning it. Course recommendation is necessary to address this problem. Most existing course recommendation methods depend on the interaction result (e.g., completion rate, grades, etc.). However, the long period required to complete a course, especially large-scale online courses in higher education, can lead to serious sparsity of interaction results. In view of this, we propose a novel course recommendation method named HGE-CRec, which utilizes context formation for heterogeneous graphs to model students and courses. HGE-CRec develops meta-path embedding simulation and meta-path weight fusion to enhance the meta-path embedding set, which can expand the learning space of the prediction model and improve the representation ability of meta-path embedding, thereby avoiding tedious manual setting of the meta-path and improving the effectiveness of the resulting recommendations. Extensive experiments show that the proposed approach has advantages over a number of existing baseline methods.https://www.mdpi.com/2076-3417/13/4/2404online learningheterogeneous graphgraph neural networkscourse recommendation system |
| spellingShingle | Zhengyang Wu Qingyu Liang Zehui Zhan Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph Applied Sciences online learning heterogeneous graph graph neural networks course recommendation system |
| title | Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph |
| title_full | Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph |
| title_fullStr | Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph |
| title_full_unstemmed | Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph |
| title_short | Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph |
| title_sort | course recommendation based on enhancement of meta path embedding in heterogeneous graph |
| topic | online learning heterogeneous graph graph neural networks course recommendation system |
| url | https://www.mdpi.com/2076-3417/13/4/2404 |
| work_keys_str_mv | AT zhengyangwu courserecommendationbasedonenhancementofmetapathembeddinginheterogeneousgraph AT qingyuliang courserecommendationbasedonenhancementofmetapathembeddinginheterogeneousgraph AT zehuizhan courserecommendationbasedonenhancementofmetapathembeddinginheterogeneousgraph |