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Fast and accurate evaluation of collaborative filtering recommendation algorithms

Polatidis, Nikolaos; Kapetanakis, Stelios; Pimenidis, Elias; Manolopoulos, Yannis

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Authors

Nikolaos Polatidis

Stelios Kapetanakis

Yannis Manolopoulos



Contributors

Ngoc Thanh Nguyen
Editor

Tien Khoa Tran
Editor

Ualsher Tukayev
Editor

Tzung-Pei Hong
Editor

Bogdan Trawiński
Editor

Edward Szczerbicki
Editor

Abstract

Collaborative filtering are recommender systems algorithms that provide personalized recommendations to users in various online environments such as movies, music, books, jokes and others. There are many such recommendation algorithms and, regarding experimental evaluations to find which algorithm performs better a lengthy process needs to take place and the time required depends on the size of the dataset and the evaluation metrics used. In this paper we present a novel method that is based on a series of steps that include random subset selections, ensemble learning and the use of well-known evaluation metrics Mean Absolute Error and Precision to identify, in a fast and accurate way, which algorithm performs the best for a given dataset. The proposed method has been experimentally evaluated using two publicly available datasets with the experimental results showing that the time required for the evaluation is significantly reduced, while the results are accurate when compared to a full evaluation cycle.

Citation

Polatidis, N., Kapetanakis, S., Pimenidis, E., & Manolopoulos, Y. (2022). Fast and accurate evaluation of collaborative filtering recommendation algorithms. In N. Thanh Nguyen, T. Khoa Tran, U. Tukayev, T. Hong, B. Trawiński, & E. Szczerbicki (Eds.), ACIIDS 2022: Intelligent Information and Database Systems (623-634). https://doi.org/10.1007/978-3-031-21743-2_50

Conference Name 14th Asian Conference on Intelligent Information and Database Systems
Conference Location Ho Chi Minh City, Vietnam
Start Date Nov 28, 2022
End Date Nov 30, 2022
Acceptance Date Mar 16, 2021
Online Publication Date Dec 9, 2022
Publication Date Dec 9, 2022
Deposit Date Apr 27, 2022
Publicly Available Date Dec 10, 2023
Electronic ISSN 1611-3349
Publisher Springer Verlag
Volume 13757 LNAI
Pages 623-634
Series Title Lecture Notes in Computer Science (LNCS, volume 13757)
Series ISSN 1611-3349; 0302-9743
Book Title ACIIDS 2022: Intelligent Information and Database Systems
Chapter Number 50
ISBN 9783031217425
DOI https://doi.org/10.1007/978-3-031-21743-2_50
Keywords Recommender Systems; Collaborative Filtering; Evaluation; Mean Absolute Error; Precision
Public URL https://uwe-repository.worktribe.com/output/9422042
Publisher URL https://link.springer.com/chapter/10.1007/978-3-031-21743-2_50
Related Public URLs https://link.springer.com/book/9783031219689

https://www.springer.com/series/558

https://link.springer.com/conference/aciids

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