article

A content-based recommendation algorithm for learning resources

Authors:

Zhaoli ZhangAuthors Info & Claims

Multimedia Systems, Volume 24, Issue 2

Pages 163 - 173

https://doi.org/10.1007/s00530-017-0539-8

Published: 01 March 2018 Publication History

Abstract

Automatic multimedia learning resources recommendation has become an increasingly relevant problem: it allows students to discover new learning resources that match their tastes, and enables the e-learning system to target the learning resources to the right students. In this paper, we propose a content-based recommendation algorithm based on convolutional neural network (CNN). The CNN can be used to predict the latent factors from the text information of the multimedia resources. To train the CNN, its input and output should first be solved. For its input, the language model is used. For its output, we propose the latent factor model, which is regularized by L1-norm. Furthermore, the split Bregman iteration method is introduced to solve the model. The major novelty of the proposed recommendation algorithm is that the text information is used directly to make the content-based recommendation without tagging. Experimental results on public databases in terms of quantitative assessment show significant improvements over conventional methods. In addition, the split Bregman iteration method which is introduced to solve the model can greatly improve the training efficiency.

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cover image Multimedia Systems

Multimedia Systems Volume 24, Issue 2

March 2018

116 pages

ISSN:0942-4962

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Copyright © Copyright © 2018 Springer-Verlag GmbH Germany, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 March 2018

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https://dl.acm.org/doi/10.1145/3674982
Benedetto ERomano GTorre IVallarino MVercelli G(2024)A Visual Comparison interface for educational videosProceedings of the 2024 International Conference on Advanced Visual Interfaces10.1145/3656650.3656699(1-5)Online publication date: 3-Jun-2024
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