Similarity-Inclusive Link Prediction With Quaternions

dc.authorid Özkan, Kemal/0000-0003-2252-2128
dc.authorid KURT, ZUHAL/0000-0003-1740-6982
dc.authorscopusid 55806648900
dc.authorscopusid 6701450919
dc.authorscopusid 36661765600
dc.authorscopusid 15081108900
dc.authorwosid Özkan, Kemal/GLU-8209-2022
dc.authorwosid KURT, ZUHAL/AAE-5182-2022
dc.contributor.author Kurt, Zuhal
dc.contributor.author Gerek, Omer Nezih
dc.contributor.author Bilge, Alper
dc.contributor.author Ozkan, Kemal
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:18:42Z
dc.date.available 2024-07-05T15:18:42Z
dc.date.issued 2021
dc.department Atılım University en_US
dc.department-temp [Kurt, Zuhal] Atilim Univ, Dept Comp Engn, Ankara, Turkey; [Gerek, Omer Nezih] Eskisehir Tech Univ, Dept Elect & Elect Engn, Eskisehir, Turkey; [Bilge, Alper] Akdeniz Univ, Dept Comp Engn, Antalya, Turkey; [Ozkan, Kemal] Eskisehir Osmangazi Univ, Dept Comp Engn, Eskisehir, Turkey en_US
dc.description Özkan, Kemal/0000-0003-2252-2128; KURT, ZUHAL/0000-0003-1740-6982 en_US
dc.description.abstract This paper proposes a Quaternion-based link prediction method, a novel representation learning method for recommendation purposes. The proposed algorithm depends on and computation with Quaternion algebra, benefiting from the expressiveness and rich representation learning capability of the Hamilton products. The proposed method depends on a link prediction approach and reveals the significant potential for performance improvement in top-N recommendation tasks. The experimental results indicate the superior performance of the approach using two quality measurements - hits rate, and coverage - on the Movielens and Hetrec datasets. Additionally, extensive experiments are conducted on three subsets of the Amazon dataset to understand the flexibility of this algorithm to incorporate different information sources and demonstrate the effectiveness of Quaternion algebra in graph-based recommendation algorithms. The proposed algorithms obtain comparatively higher performance, they are improved with similarity factors. The results show that the proposed quaternion-based algorithm can effectively deal with the deficiencies in graph-based recommender system, making it a preferable alternative among the other available methods. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.5220/0010469808420854
dc.identifier.endpage 854 en_US
dc.identifier.isbn 9789897585098
dc.identifier.scopus 2-s2.0-85130355111
dc.identifier.startpage 842 en_US
dc.identifier.uri https://doi.org/10.5220/0010469808420854
dc.identifier.uri https://hdl.handle.net/20.500.14411/1892
dc.identifier.wos WOS:000783390600093
dc.institutionauthor Kurt, Zühal
dc.language.iso en en_US
dc.publisher Scitepress en_US
dc.relation.ispartof 23rd International Conference on Enterprise Information Systems (ICEIS) -- APR 26-28, 2021 -- ELECTR NETWORK en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 1
dc.subject Graphs en_US
dc.subject Link Prediction en_US
dc.subject Recommender System en_US
dc.subject Quaternions en_US
dc.title Similarity-Inclusive Link Prediction With Quaternions en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 0
dspace.entity.type Publication
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relation.isAuthorOfPublication.latestForDiscovery c1644357-fb5e-46b5-be18-1dd9b8e84e2e
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relation.isOrgUnitOfPublication.latestForDiscovery e0809e2c-77a7-4f04-9cb0-4bccec9395fa

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