1. //
  2. Start
  3. //
  4. Publikationen
  5. Artificial Intelligence Algorithms for C...
  6. Formate
  7. //

Artificial Intelligence Algorithms for Collaborative Book Recommender Systems

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Standard

Artificial Intelligence Algorithms for Collaborative Book Recommender Systems. / Tegetmeier, Clemens; Johannssen, Arne; Chukhrova, Nataliya.
in: Annals of Data Science, Jahrgang 11, Nr. 5, 10.2024, S. 1705-1739.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Harvard

Tegetmeier, C, Johannssen, A & Chukhrova, N 2024, 'Artificial Intelligence Algorithms for Collaborative Book Recommender Systems', Annals of Data Science, Jg. 11, Nr. 5, S. 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 Okt;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, Management studies",
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",
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

KW - Management studies

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

Zuletzt angesehen

Aktivitäten

  1. Visualizing and analyzing big data sets: Results from the Student Bodies-Eating Disorders study
  2. Workshop on Stochastic Models, Statistics and Their Applications 2017
  3. Modelling biodegradability based on OECD 301D data for the design of mineralising ionic liquids
  4. Media and Migration: An Introduction and two case studies
  5. Towards an Emotional Geography of Urban Policing: Exploring the Materialization of Police Territoriality with Emotional Mapping Interviews
  6. Clustering (Spatial) Relationships to unveil small-scale Schooling Markets
  7. How teachers use digital data: a systematic review
  8. Fostering student-teachersí analysis of their own teaching with an online-based video-feedback
  9. Studium generale - 2009
  10. Non-expert AI Educators in Higher Education: How can OER support AI Teaching Practices?
  11. Towards decent platform work
  12. Language Learning in Blended-Learning Projects: Moodle, Web 2.0, and Learner Agency
  13. How working from home impairs recovery from work: Anticipated availability as a cognitive process in the stressor-detachment model
  14. 403 Access Forbidden or: A Backend of One’s Own. Hacking Spaces Toward a Cyber_Feminist_City
  15. The Relevance of New Types of Visualization for Emotional Styles in the Field of Christian Religion

Publikationen

  1. Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection
  2. Trajectory tracking using MPC and a velocity observer for flat actuator systems in automotive applications
  3. Interactions between ecosystem properties and land use clarify spatial strategies to optimize trade-offs between agriculture and species conservation
  4. Multiphase-field modeling of temperature-driven intermetallic compound evolution in an Al-Mg system for application to solid-state joining processes
  5. HAWK - hybrid question answering using linked data
  6. Erroneous examples as desirable difficulty
  7. Student Game Design for Language Learning
  8. The identification of up-And downstream industries using input-output tables and a firm-level application to minority shareholdings
  9. Automatic three-dimensional geometry and mesh generation of periodic representative volume elements for matrix-inclusion composites
  10. Head turn scaling below the threshold of perception in immersive virtual environments
  11. Implicit and explicit horizons
  12. Cue predictability changes scaling in eye-movement fluctuations
  13. Quantum computing
  14. How alloying and processing effects can influence the microstructure and mechanical properties of directly extruded thin zinc wires
  15. Constructing strangeness
  16. Public perceptions of CCS in context
  17. Cobalt in end-of-life products in the EU, where does it end up? - The MaTrace approach
  18. Systematic feature evaluation for gene name recognition
  19. Digital Business Transformation and the Changing Role of the IT Function
  20. The buffering effect of selection, optimization, and compensation strategy use on the relationship between problem solving demands and occupational well-being
  21. (How) Can didactic research find its way into the classroom? Results from a questionnaire survey on the lesson preparation and continuing professional development of German teachers
  22. Adaptive control of the nonlinear dynamic behavior of the cantilever-sample system of an atomic force microscope
  23. Non-acceptances in context
  24. Toward Data-Driven Analyses of Electronic Text Books
  25. The Benefit of Web- and Computer-Based Interventions for Stress
  26. Knowledge Decolonization à la Grounded Theory
  27. Root-root interactions: extending our perspective to be more inclusive of the range of theories in ecology and agriculture using in-vivo analyses
  28. Modelling, explaining, enacting and getting feedback: How can the acquisition of core practices in teacher education be optimally fostered?
  29. What factors enable social-ecological transformative potential? The role of learning practices, empowerment, and networking
  30. Experience from downscaling IPCC-SRES scenarios to specific national-level focus scenarios for ecosystem service management
  31. Peter's positions: a diffractive analysis of authority in a year one classroom
  32. Complex Trait-Treatment-Interaction analysis