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Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

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

Standard

Artificial Intelligence Algorithms for Collaborative Book Recommender Systems. / Tegetmeier, Clemens; Johannssen, Arne; Chukhrova, Nataliya.
In: Annals of Data Science, Vol. 11, No. 5, 10.2024, p. 1705-1739.

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

Harvard

Tegetmeier, C, Johannssen, A & Chukhrova, N 2024, 'Artificial Intelligence Algorithms for Collaborative Book Recommender Systems', Annals of Data Science, vol. 11, no. 5, pp. 1705-1739. https://doi.org/10.1007/s40745-023-00474-4

APA

Tegetmeier, C., Johannssen, A., & Chukhrova, N. (2024). Artificial Intelligence Algorithms for Collaborative Book Recommender Systems. Annals of Data Science, 11(5), 1705-1739. https://doi.org/10.1007/s40745-023-00474-4

Vancouver

Tegetmeier C, Johannssen A, Chukhrova N. Artificial Intelligence Algorithms for Collaborative Book Recommender Systems. Annals of Data Science. 2024 Oct;11(5):1705-1739. doi: 10.1007/s40745-023-00474-4

Bibtex

@article{bf8002f62fe74f88a5b962f083f87b07,
title = "Artificial Intelligence Algorithms for Collaborative Book Recommender Systems",
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.",
keywords = "Artificial intelligence, Book recommender systems, knn algorithm, Machine learning, Matrix factorization algorithm, Stochastic gradient descent method",
author = "Clemens Tegetmeier and Arne Johannssen and Nataliya Chukhrova",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2023.",
year = "2024",
month = oct,
doi = "10.1007/s40745-023-00474-4",
language = "English",
volume = "11",
pages = "1705--1739",
journal = "Annals of Data Science",
issn = "2198-5804",
publisher = "Springer Science and Business Media Deutschland GmbH",
number = "5",

}

RIS

TY - JOUR

T1 - Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

AU - Tegetmeier, Clemens

AU - Johannssen, Arne

AU - Chukhrova, Nataliya

N1 - Publisher Copyright: © The Author(s) 2023.

PY - 2024/10

Y1 - 2024/10

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Book recommender systems

KW - knn algorithm

KW - Machine learning

KW - Matrix factorization algorithm

KW - Stochastic gradient descent method

UR - http://www.scopus.com/inward/record.url?scp=85161371455&partnerID=8YFLogxK

U2 - 10.1007/s40745-023-00474-4

DO - 10.1007/s40745-023-00474-4

M3 - Journal articles

AN - SCOPUS:85161371455

VL - 11

SP - 1705

EP - 1739

JO - Annals of Data Science

JF - Annals of Data Science

SN - 2198-5804

IS - 5

ER -

DOI

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