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Deep Matrix Factorization for Learning Resources Recommendation

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

Learning should last all through people’s lives. With traditional learning, learners can meet face-to-face with their teachers or tutors. However, in some circumstances, learners cannot interact with their teachers. Learning resources (e.g., books, journals, slides, etc.) would be helpful for learners to get knowledge. With a large number of learning resources, how to select appropriate learning resources to learn is very important. In this work, a deep matrix factorization model extended from the standard matrix factorization is proposed for learning resources recommendation. We validate the proposed model on five published learning resources datasets and compare it with other well-known methods in recommender systems. The experimental results show that the proposed deep matrix factorization model works well, especially it can be a good choice for large-scale datasets.

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Notes

  1. 1.

    https://www.kaggle.com/philippsp/book-recommender-collaborative-filtering-shiny.

  2. 2.

    https://www.kaggle.com/ruchi798/bookcrossing-dataset.

  3. 3.

    https://jmcauley.ucsd.edu/data/amazon/.

  4. 4.

    https://doi.org/10.7910/DVN/AT4MNE.

  5. 5.

    https://cseweb.ucsd.edu/~jmcauley/datasets.html.

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Authors and Affiliations

  1. College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam

    Tran Thanh Dien, Nguyen Thanh-Hai & Nguyen Thai Nghe

Authors
  1. Tran Thanh Dien
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  2. Nguyen Thanh-Hai
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  3. Nguyen Thai Nghe
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Corresponding author

Correspondence to Nguyen Thai Nghe .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Dien, T.T., Thanh-Hai, N., Nghe, N.T. (2021). Deep Matrix Factorization for Learning Resources Recommendation. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88080-4

  • Online ISBN: 978-3-030-88081-1

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