A Graph-Based Recommendation Algorithm on Quaternion Algebra

dc.authorscopusid 55806648900
dc.authorscopusid 6701450919
dc.authorscopusid 36661765600
dc.authorscopusid 15081108900
dc.contributor.author Kurt,Z.
dc.contributor.author Gerek,Ö.N.
dc.contributor.author Bilge,A.
dc.contributor.author Özkan,K.
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:49:55Z
dc.date.available 2024-07-05T15:49:55Z
dc.date.issued 2022
dc.department Atılım University en_US
dc.department-temp Kurt Z., Department of Computer Engineering, Atılım University, Ankara, Turkey; Gerek Ö.N., Department of Electrical and Electronics Engineering, Eskişehir Technical University, Eskişehir, Turkey; Bilge A., Department of Computer Engineering, Akdeniz University, Antalya, Turkey; Özkan K., Department of Computer Engineering, Eskişehir Osmangazi University, Eskişehir, Turkey en_US
dc.description.abstract This study presents a novel Quaternion-based link prediction method to be used in different recommendation systems. The method performs Quaternion algebra-based computations while making use of expressive and wide-ranged learning properties of the Hamilton products. The proposed key capabilities rely on link prediction to boost performance in top-N recommendation tasks. According to the achieved experimental results, the proposed method allows for highly improved performance according to three quality measurements: (i) hits rate, (ii) coverage, and (iii) novelty; when applied to two datasets, namely the Movielens and Hetrec datasets. To assess the flexibility level of the proposed algorithm in terms of incorporating alternative sources of information, further wide-scale tests are carried out on three subsets of the Amazon dataset. Hence, the effectiveness of Quaternion algebra in graph-based recommendation algorithms is verified. The algorithms suggested here are further enhanced using similarity and dissimilarity factors between users and items, as well as ‘like’ and ‘dislike’ relationships between users and items. It is observed that this approach is adaptable by incorporating different information sources and can successfully overcome the drawbacks of conventional graph-based recommender systems. It is argued that the proposed novel idea of Quaternion-based link prediction method stands as a superior alternative to existing methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. en_US
dc.identifier.citationcount 1
dc.identifier.doi 10.1007/s42979-022-01171-4
dc.identifier.issn 2662-995X
dc.identifier.issue 4 en_US
dc.identifier.scopus 2-s2.0-85130398733
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.1007/s42979-022-01171-4
dc.identifier.uri https://hdl.handle.net/20.500.14411/4048
dc.identifier.volume 3 en_US
dc.institutionauthor Kurt, Zühal
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation.ispartof SN Computer Science en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 1
dc.subject Graphs en_US
dc.subject Link prediction en_US
dc.subject Quaternions en_US
dc.subject Recommendation algorithms en_US
dc.title A Graph-Based Recommendation Algorithm on Quaternion Algebra en_US
dc.type Article en_US
dspace.entity.type Publication
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relation.isOrgUnitOfPublication.latestForDiscovery e0809e2c-77a7-4f04-9cb0-4bccec9395fa

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