• May 15, 2019 News!Vol.7, No.5- Vol.8, No.4 has been indexed by EI (Inspec).   [Click]
  • Aug 01, 2018 News! [CFP] 2020 the annual meeting of IJMO Editorial Board, ECDMO 2020, will be held in Athens, Greece, February 15-17, 2020.   [Click]
  • Aug 05, 2019 News!Vol 9, No 5 has been published with online version. 10 original aritcles from 6 countries are published in this issue.    [Click]
General Information
    • ISSN: 2010-3697  (Online)
    • Abbreviated Title: Int. J. Model. Optim.
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET), EBSCO, etc.
    • E-mail ijmo@iacsitp.com
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2019 Vol.9(1): 18-23 ISSN: 2010-3697
DOI: 10.7763/IJMO.2019.V9.677

Deep Exercise Recommendation Model

Tuanji Gong and Xuanxia Yao
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).


Cite: Tuanji Gong and Xuanxia Yao, "Deep Exercise Recommendation Model," International Journal of Modeling and Optimization vol. 9, no. 1, pp. 18-23, 2019.

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