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Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

Research output: Journal contributions › Journal articles › Research › peer-review

Authors

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.

Original languageEnglish
JournalAnnals of Data Science
Volume11
Issue number5
Pages (from-to)1705-1739
Number of pages35
ISSN2198-5804
DOIs
Publication statusPublished - 10.2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2023.

    Research areas

  • Artificial intelligence, Book recommender systems, knn algorithm, Machine learning, Matrix factorization algorithm, Stochastic gradient descent method
  • Management studies

DOI

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