Deep Exercise Recommendation Model

Tuanji Gong, Xuanxia Yao
2019 International Journal of Modeling and Optimization  
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
more » ... nd 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. Tuanji Gong is a PhD student in computer science and technology at
doi:10.7763/ijmo.2019.v9.677 fatcat:zlghod2ugjetlghcod3onoewgq