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Cited by 3,602

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

A Survey of Collaborative Filtering Techniques

Open access
Su, Xiaoyuan, Khoshgoftaar, Taghi M.·2009·Advances in Artificial Intelligence
DOI 10.1155/2009/421425
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Abstract

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

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Citations

Cites

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Metadata

Authors
Su, X., & Khoshgoftaar, T. M.
Venue
Advances in Artificial Intelligence
Publisher
Wiley
Published
2009-10-27
Volume
2009
Pages
1-19
ISSN
  • 1687-7470 (ISSN-L)
  • 1687-7489
DOI
10.1155/2009/421425
OpenAlex
W2100235918
Cited by
3,602
References
7
FWCI
277.17
Type
journal article
Language
en
Open access
hybrid
Licence
creativecommons.org/licenses/by/3.0/ (unspecified)
Funders
Florida Atlantic University

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.