Collaborative filtering recommender systems: Methods, strengths and weaknesses

Marcel Schmidtke, Jannik Neeb and Thomas Wöhner

No 01/2025, Wirtschaftswissenschaftliche Schriften from Ernst-Abbe-Hochschule Jena – University of Applied Sciences, Department of Business Administration

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.

Keywords: Recommender systems; collaborative filtering; literature review; memory-based; model-based; Empfehlungssysteme; kollaboratives Filtern; systematische Literaturanalyse; speicherbasiert; modellbasiert (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/315753/1/1923542559.pdf (application/pdf)

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:zbw:fhjwws:315753

Access Statistics for this paper

More papers in Wirtschaftswissenschaftliche Schriften from Ernst-Abbe-Hochschule Jena – University of Applied Sciences, Department of Business Administration Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().