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Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

Author

Listed:
  • Clemens Tegetmeier

    (University of Hamburg)

  • Arne Johannssen

    (University of Hamburg)

  • Nataliya Chukhrova

    (HafenCity University)

Abstract

Book recommender systems provide personalized recommendations of books to users based on their previous searches or purchases. As online trading of books has become increasingly important in recent years, artificial intelligence (AI) algorithms are needed to recommend suitable books to users and encourage them to make purchasing decisions in the short and the long run. In this paper, we consider AI algorithms for so called collaborative book recommender systems, especially the matrix factorization algorithm using the stochastic gradient descent method and the book-based k-nearest-neighbor algorithm. We perform a comprehensive case study based on the Book-Crossing benchmark data set, and implement various variants of both AI algorithms to predict unknown book ratings and to recommend books to individual users based on the highest predicted ratings. This study aims to evaluate the quality of the implemented methods in recommending books by using selected evaluation metrics for AI algorithms.

Suggested Citation

  • Clemens Tegetmeier & Arne Johannssen & Nataliya Chukhrova, 2024. "Artificial Intelligence Algorithms for Collaborative Book Recommender Systems," Annals of Data Science, Springer, vol. 11(5), pages 1705-1739, October.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:5:d:10.1007_s40745-023-00474-4
    DOI: 10.1007/s40745-023-00474-4
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    References listed on IDEAS

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    1. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    2. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
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