research-article

Collaborative filtering recommender systems taxonomy

Authors:

Harris Papadakis,

Antonis Papagrigoriou,

Costas Panagiotakis,

Eleftherios Kosmas,

Paraskevi FragopoulouAuthors Info & Claims

Knowledge and Information Systems, Volume 64, Issue 1

Pages 35 - 74

https://doi.org/10.1007/s10115-021-01628-7

Published: 01 January 2022 Publication History

Abstract

In the era of internet access, recommender systems try to alleviate the difficulty that consumers face while trying to find items (e.g., services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of collaborative filtering recommend systems. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a collaborative filtering recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of collaborative filtering recommender systems according to their ability to efficiently handle well-known drawbacks.

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Wegmeth L(2023)Improving Recommender Systems Through the Automation of Design DecisionsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608877(1332-1338)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608877
Yu PTan ZLu GBao BEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Multi-View Graph Convolutional Network for Multimedia RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613915(6576-6585)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613915
Alharbe NRakrouki MAljohani A(2023)A collaborative filtering recommendation algorithm based on embedding representationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119380215:COnline publication date: 15-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119380
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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 64, Issue 1

Jan 2022

283 pages

ISSN:0219-1377

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© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.

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Published: 01 January 2022

Accepted: 12 November 2021

Revision received: 09 November 2021

Received: 05 January 2021

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Cited By

Wegmeth L(2023)Improving Recommender Systems Through the Automation of Design DecisionsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608877(1332-1338)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608877
Yu PTan ZLu GBao BEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Multi-View Graph Convolutional Network for Multimedia RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613915(6576-6585)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613915
Alharbe NRakrouki MAljohani A(2023)A collaborative filtering recommendation algorithm based on embedding representationExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119380215:COnline publication date: 15-Feb-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119380
Hamdollahi Oskouei SHashemzadeh M(2023)FoodRecNet: a comprehensively personalized food recommender system using deep neural networksKnowledge and Information Systems10.1007/s10115-023-01897-465:9(3753-3775)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1007/s10115-023-01897-4

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