Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recom...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Chonghuan Xu, Dongsheng Liu, Xinyao Mei
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	POI recommendation user preference user influence forgetting characteristic trajectory Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/c1bcae0aaa9f4be7858a09b80c8519e3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c1bcae0aaa9f4be7858a09b80c8519e3
record_format	dspace
spelling	oai:doaj.org-article:c1bcae0aaa9f4be7858a09b80c8519e32021-11-11T18:14:31ZExploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors10.3390/math92126732227-7390https://doaj.org/article/c1bcae0aaa9f4be7858a09b80c8519e32021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2673https://doaj.org/toc/2227-7390The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-<i>K</i> POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.Chonghuan XuDongsheng LiuXinyao MeiMDPI AGarticlePOI recommendationuser preferenceuser influenceforgetting characteristictrajectoryMathematicsQA1-939ENMathematics, Vol 9, Iss 2673, p 2673 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	POI recommendation user preference user influence forgetting characteristic trajectory Mathematics QA1-939
spellingShingle	POI recommendation user preference user influence forgetting characteristic trajectory Mathematics QA1-939 Chonghuan Xu Dongsheng Liu Xinyao Mei Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
description	The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-<i>K</i> POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.
format	article
author	Chonghuan Xu Dongsheng Liu Xinyao Mei
author_facet	Chonghuan Xu Dongsheng Liu Xinyao Mei
author_sort	Chonghuan Xu
title	Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
title_short	Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
title_full	Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
title_fullStr	Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
title_full_unstemmed	Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
title_sort	exploring an efficient poi recommendation model based on user characteristics and spatial-temporal factors
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c1bcae0aaa9f4be7858a09b80c8519e3
work_keys_str_mv	AT chonghuanxu exploringanefficientpoirecommendationmodelbasedonusercharacteristicsandspatialtemporalfactors AT dongshengliu exploringanefficientpoirecommendationmodelbasedonusercharacteristicsandspatialtemporalfactors AT xinyaomei exploringanefficientpoirecommendationmodelbasedonusercharacteristicsandspatialtemporalfactors
_version_	1718431908995530752

Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

Ejemplares similares