Latent Semantic Indexing-Based Hybrid Collaborative Filtering for Recommender Systems

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dc.authoridHorasan, Fahrettin/0000-0003-4554-9083
dc.contributor.authorHorasan, Fahrettin
dc.date.accessioned2025-01-21T16:42:18Z
dc.date.available2025-01-21T16:42:18Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractAdvances in information technologies increase the number and diversity of digital objects. This increase poses significant problems in reaching the target audience of digital products. Recommender systems (RS) that propose digital objects according to user profiles aim to deal with these problems. In collaborative recommender systems (CRS), recommendations are made considering similar digital objects. In this study, a hybrid model based on latent semantic indexing (LSI) is proposed for the CRS. User-based, item-based, and hybrid models have been developed by using the LSI, which is generally encountered in text analysis, information retrieval, and information access. These improved models were compared with the models based on the most commonly used Pearson correlation coefficient (PCC) in the CRS. Accordingly, it was observed that predictions were better in all models based on LSI. The developed models have lower computational complexity due to the dimension reduction process. Besides, the proposed hybrid model produced more accurate predictions than the user-based and the item-based models.
dc.identifier.doi10.1007/s13369-022-06704-w
dc.identifier.endpage10653
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85126001633
dc.identifier.scopusqualityQ1
dc.identifier.startpage10639
dc.identifier.urihttps://doi.org/10.1007/s13369-022-06704-w
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25048
dc.identifier.volume47
dc.identifier.wosWOS:000766427400003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofArabian Journal For Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectRecommender systems; Collaborative filtering; Latent semantic indexing; Dimension reduction
dc.titleLatent Semantic Indexing-Based Hybrid Collaborative Filtering for Recommender Systems
dc.typeArticle

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