Recommendation System Based on Heterogeneous Feature: A Survey

Recommendation systems have become an important field of research in computer science and physics. In recent years, breakthroughs have been achieved in social, biological, and research cooperation networks. With the popularization of big data and deep learning technology development, graph structure...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hui Wang, Zichun Le, Xuan Gong
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Behavior features feature graph features recommendation systems text features Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/314162410bec405081d7d321212aa9f6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:314162410bec405081d7d321212aa9f6
record_format	dspace
spelling	oai:doaj.org-article:314162410bec405081d7d321212aa9f62021-11-19T00:05:44ZRecommendation System Based on Heterogeneous Feature: A Survey2169-353610.1109/ACCESS.2020.3024154https://doaj.org/article/314162410bec405081d7d321212aa9f62020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9197624/https://doaj.org/toc/2169-3536Recommendation systems have become an important field of research in computer science and physics. In recent years, breakthroughs have been achieved in social, biological, and research cooperation networks. With the popularization of big data and deep learning technology development, graph structures are increasingly being used to represent large-scale and complex data in the real world. In this paper, we reviewed the progress made in recommendation systems research in the past 20 years and comprehensively classified recommender systems based on the heterogeneous input features. We introduced layering in the classification of recommendation systems. Furthermore, we proposed a new hierarchical classification model of recommendation systems divided into three layers: feature input, feature learning, and output layers. In the feature learning layer, existing recommendation systems were divided into graph-based, text-based, behavior-based, spatiotemporal-based, and hybrid recommendation systems. Additionally, we provided evaluation index, open-source implementation, experimental comparison and the relative merits for each recommendation method. Subsequently, future development directions of recommendation systems are discussed.Hui WangZichun LeXuan GongIEEEarticleBehavior featuresfeaturegraph featuresrecommendation systemstext featuresElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 170779-170793 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Behavior features feature graph features recommendation systems text features Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Behavior features feature graph features recommendation systems text features Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hui Wang Zichun Le Xuan Gong Recommendation System Based on Heterogeneous Feature: A Survey
description	Recommendation systems have become an important field of research in computer science and physics. In recent years, breakthroughs have been achieved in social, biological, and research cooperation networks. With the popularization of big data and deep learning technology development, graph structures are increasingly being used to represent large-scale and complex data in the real world. In this paper, we reviewed the progress made in recommendation systems research in the past 20 years and comprehensively classified recommender systems based on the heterogeneous input features. We introduced layering in the classification of recommendation systems. Furthermore, we proposed a new hierarchical classification model of recommendation systems divided into three layers: feature input, feature learning, and output layers. In the feature learning layer, existing recommendation systems were divided into graph-based, text-based, behavior-based, spatiotemporal-based, and hybrid recommendation systems. Additionally, we provided evaluation index, open-source implementation, experimental comparison and the relative merits for each recommendation method. Subsequently, future development directions of recommendation systems are discussed.
format	article
author	Hui Wang Zichun Le Xuan Gong
author_facet	Hui Wang Zichun Le Xuan Gong
author_sort	Hui Wang
title	Recommendation System Based on Heterogeneous Feature: A Survey
title_short	Recommendation System Based on Heterogeneous Feature: A Survey
title_full	Recommendation System Based on Heterogeneous Feature: A Survey
title_fullStr	Recommendation System Based on Heterogeneous Feature: A Survey
title_full_unstemmed	Recommendation System Based on Heterogeneous Feature: A Survey
title_sort	recommendation system based on heterogeneous feature: a survey
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/314162410bec405081d7d321212aa9f6
work_keys_str_mv	AT huiwang recommendationsystembasedonheterogeneousfeatureasurvey AT zichunle recommendationsystembasedonheterogeneousfeatureasurvey AT xuangong recommendationsystembasedonheterogeneousfeatureasurvey
_version_	1718420660698480640

Recommendation System Based on Heterogeneous Feature: A Survey

Ejemplares similares