TY - GEN
T1 - Hierarchical reinforcement learning for course recommendation in MOOCs
AU - Zhang, Jing
AU - Hao, Bowen
AU - Chen, Bo
AU - Li, Cuiping
AU - Chen, Hong
AU - Sun, Jimeng
N1 - Publisher Copyright:
© 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=85089107536&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089107536&partnerID=8YFLogxK
U2 - 10.1609/aaai.v33i01.3301435
DO - 10.1609/aaai.v33i01.3301435
M3 - Conference contribution
AN - SCOPUS:85089107536
T3 - 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
SP - 435
EP - 442
BT - 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
PB - American Association for Artificial Intelligence (AAAI) Press
T2 - 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
Y2 - 27 January 2019 through 1 February 2019
ER -