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Hierarchical Reinforcement Learning for Course Recommendation in MOOCs
Open accessAbstract
The proliferation of massive open online courses (MOOCs) demands an effective way of personalized course recommendation. The recent attention-based recommendation models can distinguish the effects of different historical courses when recommending different target courses. However, when a user has interests in many different courses, the attention mechanism will perform poorly as the effects of the contributing courses are diluted by diverse historical courses. To address such a challenge, we propose a hierarchical reinforcement learning algorithm to revise the user profiles and tune the course recommendation model on the revised profiles.Systematically, we evaluate the proposed model on a real dataset consisting of 1,302 courses, 82,535 users and 458,454 user enrolled behaviors, which were collected from XuetangX—one of the largest MOOCs in China. Experimental results show that the proposed model significantly outperforms the state-of-the-art recommendation models (improving 5.02% to 18.95% in terms of HR@10).
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“These application scenarios could benefit from recent advances in causal inference and discovery [39], graphbased reasoning [40], reinforcement learning [41], and speech recognition and synthesis [24], [42].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [41]
In the citing paper
“DRL can empower things with the ability to interact with and adapt to the dynamic world, making it useful in many AIoT applications (Figure 9), such as autonomous driving in smart transportation [203], [199], 3D-landmark detection of CT scans [212] and robot control [213] in smart healthcare, course recommendation in smart education [41], real-time scheduling for smart factory [214], load”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [41]
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“To this end, deep reinforcement learning-based [41] and rulebased [226] recommendation methods have been proposed.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [41]
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EdFitter: a hybrid course recommendation system using large language models
Koltcov, S., Ryumin, D., Axyonov, A., Koltsova, O. Y., Sergey, N., Surkov, A., & Semenov, K. K. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.4141
Metadata
- Authors
- Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., & Sun, J.
- Affiliations
- Renmin University of China (CN)
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 435-442
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2905101140
- Cited by
- 196
- References
- 34
- Citations by year
- 2026: 9 · 2025: 31 · 2024: 33 · 2023: 38 · 2022: 35 · 2021: 31 · 2020: 16 · 2019: 3
- FWCI
- 16.93
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61702522,, 61532021, 61772536, 61772537)
- Renmin University of China
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- course recommendation, MOOCs, hierarchical reinforcement learning, personalized recommendation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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