Artificial Intelligence Algorithms for Collaborative Book Recommender Systems
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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in: Annals of Data Science, Jahrgang 11, Nr. 5, 10.2024, S. 1705-1739.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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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 -