TY - JOUR
T1 - An interpretable framework for investigating the neighborhood effect in POI recommendation
AU - Yuan, Guangchao
AU - Singh, Munindar P.
AU - Murukannaiah, Pradeep K.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85112615750&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0255685
DO - 10.1371/journal.pone.0255685
M3 - Article
C2 - 34351995
AN - SCOPUS:85112615750
SN - 1932-6203
VL - 16
JO - PLoS ONE
JF - PLoS ONE
IS - 8
M1 - e0255685
ER -