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Collaborative filtering recommender systems: Methods, strengths and weaknesses

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  • Schmidtke, Marcel
  • Neeb, Jannik
  • Wöhner, Thomas

Abstract

Recommender systems are indispensable in e-business due to the extensive product range and the large number of niche articles. Collaborative filtering (CF) algorithms play a central role in generating personalised recommendations. There are numerous CF approaches in the literature that have specific advantages and limitations. The aim of this article is to provide a comprehensive overview of the characteristics of model- and memory-based CF methods through a systematic literature analysis of 62 scientific papers. The analysis shows that memory-based approaches are convincing due to their simple implementation and interpretability but exhibit scaling problems as well as susceptibility to data sparsity and the cold start problem. Model-based methods, on the other hand, offer greater scalability and robustness against data sparsity, but require a more complex implementation, higher computing power and complicate the interpretation of results.

Suggested Citation

  • Schmidtke, Marcel & Neeb, Jannik & Wöhner, Thomas, 2025. "Collaborative filtering recommender systems: Methods, strengths and weaknesses," Wirtschaftswissenschaftliche Schriften 01/2025, Ernst-Abbe-Hochschule Jena – University of Applied Sciences, Department of Business Administration.
    • Handle: RePEc:zbw:fhjwws:315753
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    References listed on IDEAS

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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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