TY - JOUR AU - Zhang, Jing AU - Hao, Bowen AU - Chen, Bo AU - Li, Cuiping AU - Chen, Hong AU - Sun, Jimeng PY - 2019/07/17 Y2 - 2024/03/29 TI - Hierarchical Reinforcement Learning for Course Recommendation in MOOCs JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: AI and the Web DO - 10.1609/aaai.v33i01.3301435 UR - https://ojs.aaai.org/index.php/AAAI/article/view/3815 SP - 435-442 AB - <p>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.</p><p>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).</p> ER -