An interpretable framework for investigating the neighborhood effect in POI recommendation.

Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Guangchao Yuan, Munindar P Singh, Pradeep K Murukannaiah
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/32ed5fa152ba4bb7b432a93174314ca5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:32ed5fa152ba4bb7b432a93174314ca5
record_format	dspace
spelling	oai:doaj.org-article:32ed5fa152ba4bb7b432a93174314ca52021-12-02T20:15:11ZAn interpretable framework for investigating the neighborhood effect in POI recommendation.1932-620310.1371/journal.pone.0255685https://doaj.org/article/32ed5fa152ba4bb7b432a93174314ca52021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255685https://doaj.org/toc/1932-6203Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect, which captures a user's POI visiting behavior based on the user's preference not only to a POI, but also to the POI's neighborhood. To provide an interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp data set to evaluate how different aspects of the neighborhood effect affect a user's POI visiting behavior. Second, we propose a deep learning-based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization-based POI recommendation techniques.Guangchao YuanMunindar P SinghPradeep K MurukannaiahPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255685 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guangchao Yuan Munindar P Singh Pradeep K Murukannaiah An interpretable framework for investigating the neighborhood effect in POI recommendation.
description	Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect, which captures a user's POI visiting behavior based on the user's preference not only to a POI, but also to the POI's neighborhood. To provide an interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp data set to evaluate how different aspects of the neighborhood effect affect a user's POI visiting behavior. Second, we propose a deep learning-based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization-based POI recommendation techniques.
format	article
author	Guangchao Yuan Munindar P Singh Pradeep K Murukannaiah
author_facet	Guangchao Yuan Munindar P Singh Pradeep K Murukannaiah
author_sort	Guangchao Yuan
title	An interpretable framework for investigating the neighborhood effect in POI recommendation.
title_short	An interpretable framework for investigating the neighborhood effect in POI recommendation.
title_full	An interpretable framework for investigating the neighborhood effect in POI recommendation.
title_fullStr	An interpretable framework for investigating the neighborhood effect in POI recommendation.
title_full_unstemmed	An interpretable framework for investigating the neighborhood effect in POI recommendation.
title_sort	interpretable framework for investigating the neighborhood effect in poi recommendation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/32ed5fa152ba4bb7b432a93174314ca5
work_keys_str_mv	AT guangchaoyuan aninterpretableframeworkforinvestigatingtheneighborhoodeffectinpoirecommendation AT munindarpsingh aninterpretableframeworkforinvestigatingtheneighborhoodeffectinpoirecommendation AT pradeepkmurukannaiah aninterpretableframeworkforinvestigatingtheneighborhoodeffectinpoirecommendation AT guangchaoyuan interpretableframeworkforinvestigatingtheneighborhoodeffectinpoirecommendation AT munindarpsingh interpretableframeworkforinvestigatingtheneighborhoodeffectinpoirecommendation AT pradeepkmurukannaiah interpretableframeworkforinvestigatingtheneighborhoodeffectinpoirecommendation
_version_	1718374574520795136

An interpretable framework for investigating the neighborhood effect in POI recommendation.

Ejemplares similares