Liang, K.; Zhang, G.; Guo, J.; Li, W. An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation. Electronics2023, 12, 4939.
Liang, K.; Zhang, G.; Guo, J.; Li, W. An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation. Electronics 2023, 12, 4939.
Liang, K.; Zhang, G.; Guo, J.; Li, W. An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation. Electronics2023, 12, 4939.
Liang, K.; Zhang, G.; Guo, J.; Li, W. An Actor-Critic Hierarchical Reinforcement Learning Model for Course Recommendation. Electronics 2023, 12, 4939.
Abstract
Abstract—Online learning platforms provide diverse course resources, but this often result in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, Existing course recommendation methods face the challenge in accurately recommending the target course that is most relevant to the user, because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the Actor-Critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendation effectively. Furthermore, we propose a policy gradient based on temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and improves the accuracy of the recommendation. We evaluate the proposed model on two real datasets, and the experimental results show that the proposed model is significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
