Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions

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dc.contributor.authorKumar, Balrajcs
dc.contributor.authorSharma, Neerajcs
dc.contributor.authorSharma, Bhishamcs
dc.contributor.authorHerencsár, Norbertcs
dc.contributor.authorSrivastava, Gautamcs
dc.coverage.issue5cs
dc.coverage.volume23cs
dc.date.issued2023-02-23cs
dc.description.abstractRecommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others. However, these recommender systems are lacking in producing quality recommendations owing to sparsity issues. Keeping this in mind, the present study introduces a hybrid recommendation model for recommending music artists to users which is hierarchical Bayesian in nature, known as Relational Collaborative Topic Regression with Social Matrix Factorization (RCTR–SMF). This model makes use of a lot of auxiliary domain knowledge and provides seamless integration of Social Matrix Factorization and Link Probability Functions into Collaborative Topic Regression-based recommender systems to attain better prediction accuracy. Here, the main emphasis is on examining the effectiveness of unified information related to social networking and an item-relational network structure in addition to item content and user-item interactions to make predictions for user ratings. RCTR–SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. Furthermore, this article exhibits the proposed model performance on a large real-world social media dataset. The proposed model provides a recall of 57% and demonstrates its superiority over other state-of-the-art recommendation algorithms.en
dc.formattextcs
dc.format.extent1-20cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSENSORS. 2023, vol. 23, issue 5, p. 1-20.en
dc.identifier.doi10.3390/s23052495cs
dc.identifier.issn1424-8220cs
dc.identifier.orcid0000-0002-9504-2275cs
dc.identifier.other182960cs
dc.identifier.researcheridA-6539-2009cs
dc.identifier.scopus23012051100cs
dc.identifier.urihttp://hdl.handle.net/11012/209275
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSENSORScs
dc.relation.urihttps://www.mdpi.com/1424-8220/23/5/2495cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1424-8220/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectcollaborative filteringen
dc.subjecttopic modellingen
dc.subjectrecommendation systemen
dc.subjectcollaborative topic regressionen
dc.subjectsocial matrix factorizationen
dc.subjectsocial networken
dc.subjectitem network structureen
dc.titleHybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functionsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-182960en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:30en
sync.item.modts2025.01.17 15:29:03en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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