Name
Abstract | ||
|---|---|---|
Online course recommendation is an extremely relevant ingredient for the efficiency of e-learning. The current recommendation methods cannot guarantee the effectiveness and accuracy of course recommendation, especially when a user has enrolled in many different courses. Because these methods fail to distinguish the most relevant historical courses, which can contribute to predicting the target course that indeed reflects the user’s interests from her sequential learning behaviors. In this paper, we propose a context-aware reinforcement learning method, named Hierarchical and Recurrent Reinforcement Learning (HRRL), to efficiently reconstruct user profiles for course recommendation. The key ingredient of our scheme is the novel interaction between an attention-based recommendation model and a profile reviser with Recurrent Reinforcement Learning (RRL) that exploits temporal context. To this aim, a contextual policy gradient with approximation is proposed for RRL. By employing RRL in hierarchical tasks of revising user profiles, the proposed HRRL model enables reliable convergence in revising policy learning and improves the recommendation accuracy. We demonstrate the effectiveness of our proposed method by experiments on two open online courses datasets. Empirical results show that HRRL significantly outperforms state-of-the-art baselines. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.asoc.2022.109189 | Applied Soft Computing |
Keywords | DocType | Volume |
Recommender systems,Reinforcement learning,Policy gradient,Attention mechanism | Journal | 125 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanguo Lin | 1 | 0 | 0.68 |
| Fan Lin | 2 | 67 | 15.98 |
| Lvqing Yang | 3 | 13 | 4.34 |
| Wenhua Zeng | 4 | 136 | 14.83 |
| Yong Liu | 5 | 290 | 19.62 |
| Pengcheng Wu | 6 | 654 | 30.60 |