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Recommendations in E-Commerce Systems Based on Deep Matrix Factorization

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1688))

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Abstract

E-commerce systems (including online shopping, entertainment, etc.) play an increasingly important role and have become popular in digital life. These systems have also become one of the cores, and vital issues for many businesses, especially from the recent COVID-19 pandemic, the importance of online e-commerce systems are very necessary. Techniques in recommendation systems are widely used to support users in finding suitable products/items in online systems. This work proposes using deep matrix factorization for recommendation in online e-commerce systems. We provide a detailed architecture of a deep matrix factorization as well as make a comparison with the standard matrix factorization model. Experimental results on ten published data sets show that the deep matrix factorization model can work well for recommendations in online e-commerce systems.

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Notes

  1. 1.

    https://nijianmo.github.io/amazon/index.html..

  2. 2.

    https://www.kaggle.com/datasets/CooperUnion/anime-recommendations-database..

  3. 3.

    https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset..

  4. 4.

    https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions..

  5. 5.

    https://grouplens.org/datasets/movielens/.

  6. 6.

    https://www.kaggle.com/datasets/uciml/restaurant-data-with-consumer-ratings..

  7. 7.

    https://www.kaggle.com/datasets/retailrocket/ecommerce-dataset..

  8. 8.

    https://webscope.sandbox.yahoo.com/catalog.php?datatype=r.

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Authors and Affiliations

  1. Can Tho University, Can Tho city, Vietnam

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

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

Correspondence to Tran Thanh Dien .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Seoul, Korea (Republic of)

    Tai M. Chung

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Thai-Nghe, N., Thanh-Hai, N., Dien, T.T. (2022). Recommendations in E-Commerce Systems Based on Deep Matrix Factorization. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_28

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  • DOI: https://doi.org/10.1007/978-981-19-8069-5_28

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  • Online ISBN: 978-981-19-8069-5

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