南方科技大学知识苑(SUSTech KC): Hyper-relational knowledge graph neural network for next POI recommendation

题名	Hyper-relational knowledge graph neural network for next POI recommendation
作者	Zhang, Jixiao 1; Li, Yongkang 2; Zou, Ruotong 1; Zhang, Jingyuan 1; Jiang, Renhe 3; Fan, Zipei 4; Song, Xuan 4
通讯作者	Jiang, Renhe; Fan, Zipei; Song, Xuan
发表日期	2024-07-01
DOI	10.1007/s11280-024-01279-y
发表期刊	WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 影响因子和分区
ISSN	1386-145X
EISSN	1573-1413
卷号	27 期号:4
摘要	With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing works employ Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These approaches primarily focus on modeling the pair-wise relations in LBSN to enrich the semantics and thereby relieve the data sparsity issue. However, existing approaches seldom consider the hyper-relations in LBSN, such as the mobility relation (a 3-ary relation: user-POI-time). This makes the model hard to exploit the semantics accurately. In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity.To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed to maintain and exploit the rich semantics of hyper-relations. Then we proposed a Hypergraph Neural Network to utilize the structural information of HKG in a cohesive way. In addition, a self-attention network is used to leverage sequential information and make personalized recommendations. Furthermore, side information, essential in reducing data sparsity by providing background knowledge of POIs, is not fully utilized in current methods. In light of this, we extended the current dataset with available side information to further lessen the impact of data sparsity. Results of experiments on four real-world LBSN datasets demonstrate the effectiveness of our approach compared to existing state-of-the-art methods. Our implementation is available at https://github.com/aeroplanepaper/HKG.
关键词	Next POI recommendation Spatio-temporal data Recommender systems
相关链接	[来源记录]
收录类别	SCI ; EI
语种	英语
学校署名	第一
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:001263395100001
出版者	SPRINGER
EI入藏号	20242816675012
EI主题词	Graph neural networks ; Knowledge graph ; Semantics ; Social sciences computing ; User profile
EI分类号	Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Computer Applications:723.5
ESI学科分类	COMPUTER SCIENCE
来源库	Web of Science
引用统计	被引频次[WOS]：2
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/787027
专题	工学院_计算机科学与工程系
作者单位	1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China 2.Univ Amsterdam, Amsterdam, Netherlands 3.Univ Tokyo, Ctr Spatial Informat Sci, Tokyo, Japan 4.Jilin Univ, Sch Artificial Intelligence, Changchun, Peoples R China
第一作者单位	计算机科学与工程系
第一作者的第一单位	计算机科学与工程系
推荐引用方式 GB/T 7714	Zhang, Jixiao,Li, Yongkang,Zou, Ruotong,et al. Hyper-relational knowledge graph neural network for next POI recommendation[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2024,27(4).
APA	Zhang, Jixiao.,Li, Yongkang.,Zou, Ruotong.,Zhang, Jingyuan.,Jiang, Renhe.,...&Song, Xuan.(2024).Hyper-relational knowledge graph neural network for next POI recommendation.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,27(4).
MLA	Zhang, Jixiao,et al."Hyper-relational knowledge graph neural network for next POI recommendation".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 27.4(2024).