Title:	IMPROVING DEEP MATRIX FACTORIZATION WITH NORMALIZED CROSS ENTROPY LOSS FUNCTION FOR GRAPH-BASED MOOC RECOMMENDATION
Author(s):	Thanh Le, Vinh Vo, Khai Nguyen and Bac Le
ISBN:	978-989-8704-21-4
Editors:	Yingcai Xiao, Ajith P. Abraham and Jörg Roth
Year:	2020
Edition:	Single
Keywords:	MOOCs, Recommendation, Deep Matrix Factorization, Graph
Type:	Full
First Page:	141
Last Page:	148
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Nowadays, with the fast growth of the Internet, the useful role of learning online is getting increasingly popular. MOOC platforms such as Coursera, Edx, Udemy, etc. are attracting many students from all over the world, with thousands of courses constantly continually being opened and updated. This raises the question of how to suggest courses that learners are interested in. To tackle this problem, we apply the Deep matrix Factorization model to the course suggestion along with the improved loss function. The experiment shows that our course recommendation system achieves better NDCG for top K courses than other methods. And the loss function has improved in NDCG measurement compared to the original DMF model.
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