TY - GEN
T1 - Estimating potential customers anywhere and anytime based on location-based social networks
AU - Hsieh, Hsun Ping
AU - Li, Cheng Te
AU - Lin, Shou De
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Acquiring the knowledge about the volume of customers for places and time of interest has several benefits such as determining the locations of new retail stores and planning advertising strategies. This paper aims to estimate thenumber of potential customers of arbitrary query locations and any time of interest in modern urban areas. Our idea is to consider existing established stores as a kind of sensors because the near-by human activities of the retail stores characterize the geographical properties, mobility patterns, and social behaviors of the target customers. To tackle the task based on store sensors, we develop a method called Potential Customer Estimator (PCE), which models the spatial and temporal correlation between existing stores and query locations using geographical, mobility, and features on location-based social networks. Experiments conducted on NYC Foursquare and Gowalla data, with three popular retail stores, Starbucks, McDonald’s, and Dunkin’ Donuts exhibit superior results over state-of-the-art approaches.
AB - Acquiring the knowledge about the volume of customers for places and time of interest has several benefits such as determining the locations of new retail stores and planning advertising strategies. This paper aims to estimate thenumber of potential customers of arbitrary query locations and any time of interest in modern urban areas. Our idea is to consider existing established stores as a kind of sensors because the near-by human activities of the retail stores characterize the geographical properties, mobility patterns, and social behaviors of the target customers. To tackle the task based on store sensors, we develop a method called Potential Customer Estimator (PCE), which models the spatial and temporal correlation between existing stores and query locations using geographical, mobility, and features on location-based social networks. Experiments conducted on NYC Foursquare and Gowalla data, with three popular retail stores, Starbucks, McDonald’s, and Dunkin’ Donuts exhibit superior results over state-of-the-art approaches.
UR - http://www.scopus.com/inward/record.url?scp=84959371759&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959371759&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23525-7_35
DO - 10.1007/978-3-319-23525-7_35
M3 - Conference contribution
AN - SCOPUS:84959371759
SN - 9783319235240
SN - 9783319235240
SN - 9783319235240
SN - 9783319235240
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 576
EP - 592
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015
A2 - Costa, Vitor Santos
A2 - Soares, Carlos
A2 - Appice, Annalisa
A2 - Appice, Annalisa
A2 - Rodrigues, Pedro Pereira
A2 - Costa, Vitor Santos
A2 - Soares, Carlos
A2 - Gama, João
A2 - Jorge, Alípio
A2 - Rodrigues, Pedro Pereira
A2 - Gama, João
A2 - Costa, Vitor Santos
A2 - Jorge, Alípio
A2 - Appice, Annalisa
A2 - Rodrigues, Pedro Pereira
A2 - Gama, João
A2 - Appice, Annalisa
A2 - Soares, Carlos
A2 - Jorge, Alípio
A2 - Gama, João
A2 - Rodrigues, Pedro Pereira
A2 - Costa, Vitor Santos
A2 - Soares, Carlos
A2 - Jorge, Alípio
PB - Springer Verlag
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015
Y2 - 7 September 2015 through 11 September 2015
ER -