A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations

In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KA...

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Autores principales:	Dehai Zhang, Xiaobo Yang, Linan Liu, Qing Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	knowledge graph personalized recommendation attention aggregation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/5542755bebeb416782e63d307c64ac60
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id	oai:doaj.org-article:5542755bebeb416782e63d307c64ac60
record_format	dspace
spelling	oai:doaj.org-article:5542755bebeb416782e63d307c64ac602021-11-11T15:24:11ZA Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations10.3390/app1121104322076-3417https://doaj.org/article/5542755bebeb416782e63d307c64ac602021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10432https://doaj.org/toc/2076-3417In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a knowledge graph-enhanced attention aggregation network for making recommendations. This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is the attention aggregation network, which collect the user’s interaction history and captures the user’s preference for each item. The second is the knowledge graph-embedded model, which aims to integrate the knowledge. The semantic information of the nodes and edges in the graph is mapped to the low-dimensional vector space. The final part is the information interaction unit, which is used for fusing the features of two vectors. Experiments showed that our model achieved a stable improvement compared to the baseline model in making recommendations for movies, books, and music.Dehai ZhangXiaobo YangLinan LiuQing LiuMDPI AGarticleknowledge graphpersonalized recommendationattention aggregationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10432, p 10432 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	knowledge graph personalized recommendation attention aggregation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	knowledge graph personalized recommendation attention aggregation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Dehai Zhang Xiaobo Yang Linan Liu Qing Liu A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
description	In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a knowledge graph-enhanced attention aggregation network for making recommendations. This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is the attention aggregation network, which collect the user’s interaction history and captures the user’s preference for each item. The second is the knowledge graph-embedded model, which aims to integrate the knowledge. The semantic information of the nodes and edges in the graph is mapped to the low-dimensional vector space. The final part is the information interaction unit, which is used for fusing the features of two vectors. Experiments showed that our model achieved a stable improvement compared to the baseline model in making recommendations for movies, books, and music.
format	article
author	Dehai Zhang Xiaobo Yang Linan Liu Qing Liu
author_facet	Dehai Zhang Xiaobo Yang Linan Liu Qing Liu
author_sort	Dehai Zhang
title	A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
title_short	A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
title_full	A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
title_fullStr	A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
title_full_unstemmed	A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
title_sort	knowledge graph-enhanced attention aggregation network for making recommendations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5542755bebeb416782e63d307c64ac60
work_keys_str_mv	AT dehaizhang aknowledgegraphenhancedattentionaggregationnetworkformakingrecommendations AT xiaoboyang aknowledgegraphenhancedattentionaggregationnetworkformakingrecommendations AT linanliu aknowledgegraphenhancedattentionaggregationnetworkformakingrecommendations AT qingliu aknowledgegraphenhancedattentionaggregationnetworkformakingrecommendations AT dehaizhang knowledgegraphenhancedattentionaggregationnetworkformakingrecommendations AT xiaoboyang knowledgegraphenhancedattentionaggregationnetworkformakingrecommendations AT linanliu knowledgegraphenhancedattentionaggregationnetworkformakingrecommendations AT qingliu knowledgegraphenhancedattentionaggregationnetworkformakingrecommendations
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A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations

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