Recommender Systems: Collaborative Filtering and Content-based Recommender System

Xuechao Yan; Shuhan Qi; Chang Chen

doi:10.54254/2755-2721/2/20220658

Research Article

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Published on 22 March 2023

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Yan,X.;Qi,S.;Chen,C. (2023). Recommender Systems: Collaborative Filtering and Content-based Recommender System. Applied and Computational Engineering,2,937-942.

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Recommender Systems: Collaborative Filtering and Content-based Recommender System

Xuechao Yan ¹, Shuhan Qi ², Chang Chen ³

¹ Department of Informatics, King’s College London, London, WC2R 2LS, United Kingdom
² Northeast YuCai Bilingual School, Shenyang ,110000, China
³ Department of Informatics, King’s College London, London, WC2R 2LS, United Kingdom

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2/20220658

Abstract

There are three algorithms of recommender systems proposed by this paper, which are item collaborative filtering(itemCF), user collaborative filtering(useCF) and content-based recommender system(CBRS). The principal goal of this paper is to try to ascertain which algorithm has the highest precision, after training based on the same dataset. In accordance with the data we chose and ceaseless testing, we observe itemCF contains the most accurate rate. However, we theoretically and empirically conceive each algorithm owns different advantages and drawbacks, should be used in the specific circumstance.

Keywords

collaborative filtering, content-based recommender system., recommender systems

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References

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Cite this article

Yan,X.;Qi,S.;Chen,C. (2023). Recommender Systems: Collaborative Filtering and Content-based Recommender System. Applied and Computational Engineering,2,937-942.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

Conference website: https://www.confcds.org/

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)

Conference date: 16 July 2022

Editor：Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.2

ISSN：2755-2721(Print) / 2755-273X(Online)

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