Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

Clemens Tegetmeier (), Arne Johannssen () and Nataliya Chukhrova ()
Additional contact information
Clemens Tegetmeier: University of Hamburg
Arne Johannssen: University of Hamburg
Nataliya Chukhrova: HafenCity University

Annals of Data Science, 2024, vol. 11, issue 5, No 11, 1705-1739

Abstract: 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 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-023-00474-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:11:y:2024:i:5:d:10.1007_s40745-023-00474-4

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-023-00474-4

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().