Abstract—In online education scenario, recommending
exercises for students is an attractive research topic. In this
paper, we propose a new hybrid recommendation model that
combines deep collaborative filtering (DeepCF) component
with wide linear component. The former incorporates stacked
denoising auto-encoder(SDAE) into matrix factorization and
the latter is general linear component. In DeepCF component,
we employ SDAE to learn low dimension latent feature of a
student’s feature and an item’s feature and use matrix
factorization method to predict the rating that a student rates
an item. In wide linear model, we incorporate some meta
properties of an item, such as difficulty, type and knowledge
components(KCs). The two components are combined by
linear approach. We use negative sampling method to generate
the training dataset. An item is corrupted by Gaussian noise
and is feed into the SDAE net ,which consists of encoder and
decoder with multiple layers. We use tightly couple model to
combine SDAE model and collaborative filter model.
Experimental results show that the proposed model achieves a
10% relative improvement in AUC metric compared to the
traditional collaborative filter method.
Index Terms—Deep collaborative filtering, recommend
system, stacked denoising autoencoder, exercise.
The authors are with the School of Computer and Communication
Engineering, University of Science and Technology Beijing 10083,China
(e-mail: gongtuanji@foxmail.com,yaoxuanxia@163.com).
[PDF]
Cite: Tuanji Gong and Xuanxia Yao, "Deep Exercise Recommendation Model," International Journal of Modeling and Optimization vol. 9, no. 1, pp. 18-23, 2019.
