TY - GEN
T1 - Where is the largest market
T2 - 10th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and 10th IEEE International Conference on Autonomic and Trusted Computing, ATC 2013
AU - Yu, Zhiyong
AU - Zhang, Daqing
AU - Yang, Dingqi
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Ranking areas by popularity of a business category is an essential problem for business planning. Traditional approaches rely on economic and demographic factors nearby. However, the acquisition of relevant data is usually expensive. In this paper we propose a novel approach to address this problem by exploiting user-generated contents from location based social networks, which are cheap, fine-grained, and abundant. Particularly, by analyzing a dataset collected from Foursquare in Paris, we attain the customer distribution across all categories in each area. With the help of data mining methods, the popularity (i.e., the number of customers) of a particular business category can be estimated from popularities of other nearby categories, and then can be ranked accordingly. The evaluation shows that these methods significantly outperform the passenger volume based method.
AB - Ranking areas by popularity of a business category is an essential problem for business planning. Traditional approaches rely on economic and demographic factors nearby. However, the acquisition of relevant data is usually expensive. In this paper we propose a novel approach to address this problem by exploiting user-generated contents from location based social networks, which are cheap, fine-grained, and abundant. Particularly, by analyzing a dataset collected from Foursquare in Paris, we attain the customer distribution across all categories in each area. With the help of data mining methods, the popularity (i.e., the number of customers) of a particular business category can be estimated from popularities of other nearby categories, and then can be ranked accordingly. The evaluation shows that these methods significantly outperform the passenger volume based method.
KW - Business site selection
KW - Location based social networks
KW - Market size estimation
KW - Popularity ranking
UR - https://www.scopus.com/pages/publications/84894153999
U2 - 10.1109/UIC-ATC.2013.84
DO - 10.1109/UIC-ATC.2013.84
M3 - Conference contribution
AN - SCOPUS:84894153999
SN - 9781479924813
T3 - Proceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013
SP - 157
EP - 162
BT - Proceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013
Y2 - 18 December 2013 through 21 December 2013
ER -