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A Context-Aware Recommendation Framework in E-Learning Environment

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Future Data and Security Engineering (FDSE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9446))

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Abstract

The explosion of world-wide-web has offered people a large number of online courses, e-classes and e-schools. Such e-learning applications contain a wide variety of learning materials which make learners confused to select. In order to address this problem, in this paper we propose a context-aware recommendation framework to suggest a number of suitable learning materials for learners. In the proposed approach, firstly we present a method to determine contextual information implicitly. We then describe a technique to gain ratings from the study results data of learners. Finally, we propose two methods to predict and recommend potential items to active users. The first one is STI-GB for context-aware collaborative filtering (CACF) with contextual modeling approach combined from graph-based clustering technique and matrix factorization (MF). The second one is AVG which predicts ratings based on average calculation method. Experimental results reveal that the proposed consistently outperforms ISMF (combination of Item Splitting and MF), context-aware matrix factorization (CAMF) in terms of prediction accuracy.

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Acknowledgments

This research is the output of the project “Context-aware Recommender System and applying for E-Learning Recommendation” under grant number D2013-04 which belongs to University of Information Technology - Vietnam National University Ho Chi Minh City.

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Authors and Affiliations

  1. University of Information Technology, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Phung Do, Hung Nguyen & Vu Thanh Nguyen

  2. University of Science, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tran Nam Dung

Authors
  1. Phung Do
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  2. Hung Nguyen
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  3. Vu Thanh Nguyen
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  4. Tran Nam Dung
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Corresponding authors

Correspondence to Phung Do or Vu Thanh Nguyen .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University Linz, Linz, Austria

    Roland Wagner

  3. Johannes Kepler University Linz, Linz, Austria

    Josef Küng

  4. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Nam Thoai

  5. Hosei University, Tokyo, Japan

    Makoto Takizawa

  6. University of Vienna, Vienna, Austria

    Erich Neuhold

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Do, P., Nguyen, H., Nguyen, V.T., Dung, T.N. (2015). A Context-Aware Recommendation Framework in E-Learning Environment. In: Dang, T., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds) Future Data and Security Engineering. FDSE 2015. Lecture Notes in Computer Science(), vol 9446. Springer, Cham. https://doi.org/10.1007/978-3-319-26135-5_20

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  • DOI: https://doi.org/10.1007/978-3-319-26135-5_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26134-8

  • Online ISBN: 978-3-319-26135-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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