Evolving hierarchical and tag information via the deeply enhanced weighted non-negative matrix factorization of rating predictions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kutlimuratov, A. | - |
dc.contributor.author | Abdusalomov, A. | - |
dc.contributor.author | Whangbo, T.K. | - |
dc.date.available | 2020-12-16T01:40:35Z | - |
dc.date.created | 2020-12-02 | - |
dc.date.issued | 2020-11 | - |
dc.identifier.issn | 2073-8994 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/79352 | - |
dc.description.abstract | Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI AG | - |
dc.relation.isPartOf | Symmetry | - |
dc.title | Evolving hierarchical and tag information via the deeply enhanced weighted non-negative matrix factorization of rating predictions | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000594518500001 | - |
dc.identifier.doi | 10.3390/sym12111930 | - |
dc.identifier.bibliographicCitation | Symmetry, v.12, no.11, pp.1 - 17 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85096587837 | - |
dc.citation.endPage | 17 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | Symmetry | - |
dc.citation.volume | 12 | - |
dc.citation.number | 11 | - |
dc.contributor.affiliatedAuthor | Kutlimuratov, A. | - |
dc.contributor.affiliatedAuthor | Abdusalomov, A. | - |
dc.contributor.affiliatedAuthor | Whangbo, T.K. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Deep factorization | - |
dc.subject.keywordAuthor | Hierarchical information | - |
dc.subject.keywordAuthor | Recommendation system | - |
dc.subject.keywordAuthor | Tag information | - |
dc.subject.keywordAuthor | Weighted non-negative matrix factorization | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.