Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey
Yükleniyor...
Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Recommender Systems (RS) are established to deal with the preferences of users to enhance their experience and interest in innumerable online applications by streamlining the stress persuaded by the reception of excessive information through the recommendation methods. Although researches have put a lot of efforts in making recommendation processes accurate, specific, and personalized; different issues like cold start, data sparsity or gray sheep etc., still pop up in one or the other form of challenges. Recently, exploitation of Knowledge Graph (KG)-based data as Side Information in recommendation methods has revealed as a sign of resolution to the corresponding challenges; and thus, acquired incredible focus, applicability, and popularity. The incorporation of KG in recommendation has not only effectively alleviated the contrasting challenges, but also has provided specific, accurate, personalized and explainable recommendations about the target items to the end users. In this paper, we explore well-known RSs, popular knowledge repositories, benchmark datasets, recommendation methods, and future research dimensions about the current research. Intuitively, we investigate recommendation methods and associated datasets with respect to the corresponding application scenarios in a categorical way.
Açıklama
Acknowledgements The work was supported in part by the Basic Research Program of Jiangsu Province (BK20191274) and the National Natural Science Foundation of China (62176121 and 61772269) .
Anahtar Kelimeler
Categorization, Knowledge Graph, Side Information, Large-Scale, Dbpedia, Systems
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
206
Sayı
Künye
Khan, N., Ma, Z., Ullah, A., & Polat, K. (2022). Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey. Expert Systems with Applications, 206, 117737.
