An Approach to Semantic-Aware Heterogeneous Network Embedding for Recommender Systems

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dc.authoridLin, Jerry Chun-Wei/0000-0001-8768-9709
dc.authoridYun, Unil/0000-0002-3720-0861
dc.authorwosidNguyen, Ngoc Thanh/A-5855-2008
dc.contributor.authorPham, Phu
dc.contributor.authorNguyen, Loan T. T.
dc.contributor.authorNguyen, Ngoc-Thanh
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYun, Unil
dc.contributor.authorLin, Jerry Chun-Wei
dc.contributor.authorVo, Bay
dc.date.accessioned2024-05-19T14:42:23Z
dc.date.available2024-05-19T14:42:23Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractRecent studies on heterogeneous information network (HIN) embedding-based recommendations have encountered challenges. These challenges are related to the data heterogeneity of the associated unstructured attribute or content (e.g., text-based summary/description) of users and items in the context of HIN. In order to address these challenges, in this article, we propose a novel approach of semantic-aware HIN embedding-based recommendation, called SemHE4Rec. In our proposed SemHE4Rec model, we define two embedding techniques for efficiently learning the representations of both users and items in the context of HIN. These rich-structural user and item representations are then used to facilitate the matrix factorization (MF) process. The first embedding technique is a traditional co-occurrence representation learning (CoRL) approach which aims to learn the co-occurrence of structural features of users and items. These structural features are represented for their interconnections in terms of meta-paths. In order to do that, we adopt the well-known meta-path-based random walk strategy and heterogeneous Skip-gram architecture. The second embedding approach is a semantic-aware representation learning (SRL) method. The SRL embedding technique is designed to focus on capturing the unstructured semantic relations between users and item content for the recommendation task. Finally, all the learned representations of users and items are then jointly combined and optimized while integrating with the extended MF for the recommendation task. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed SemHE4Rec in comparison with the recent state-of-the-art HIN embedding-based recommendation techniques, and reveal that the joint text-based and co-occurrence-based representation learning can help to improve the recommendation performance.en_US
dc.identifier.doi10.1109/TCYB.2022.3233819
dc.identifier.endpage6040en_US
dc.identifier.issn2168-2267
dc.identifier.issn2168-2275
dc.identifier.issue9en_US
dc.identifier.pmid37021984en_US
dc.identifier.scopus2-s2.0-85147262849en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage6027en_US
dc.identifier.urihttps://doi.org10.1109/TCYB.2022.3233819
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5236
dc.identifier.volume53en_US
dc.identifier.wosWOS:001051789100053en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Cyberneticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectHeterogeneous Information Network (Hin)en_US
dc.subjectNetwork Embeddingen_US
dc.subjectRecommendation Systemen_US
dc.titleAn Approach to Semantic-Aware Heterogeneous Network Embedding for Recommender Systemsen_US
dc.typeArticleen_US

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