TY - GEN
T1 - Mining location-based service data for feature construction in retail store recommendation
AU - Chen, Tsung Yi
AU - Chen, Lyu Cian
AU - Chen, Yuh Min
PY - 2017/1/1
Y1 - 2017/1/1
N2 - In recent years, with the popularization of mobile network, the location- based service (LBS) has made great strides, becoming an efficient marketing instrument for enterprises. For the retail business, good selections of store and appropriate marketing techniques are critical to increasing the profit. However, it is difficult to select the retail store because there are numerous considerations and the analysis was short of metadata in the past. Therefore, this study uses LBS, and provides a recommendation method for retail store selection by analyzing the relationship between the user track and point-of-interest (POI). This study uses regional relevance analysis and human mobility construction to establish the feature values of retail store recommendation. This study proposes (1) architecture of the data model available for retail store recommendation by influential layers of LBS; (2) System-based solution for recommendation of retail stores, adopts the influential factors with specified data in LBS and filtered by industrial types; (3) Industry density, area categories and region/industry clustering methods of POIs. Uses KDE and KMeans to calculate the effect of regional functionality on the retail store selection, similarity is used to calculate the industry category relation, and consumption capacity is considered to state saturation feature.
AB - In recent years, with the popularization of mobile network, the location- based service (LBS) has made great strides, becoming an efficient marketing instrument for enterprises. For the retail business, good selections of store and appropriate marketing techniques are critical to increasing the profit. However, it is difficult to select the retail store because there are numerous considerations and the analysis was short of metadata in the past. Therefore, this study uses LBS, and provides a recommendation method for retail store selection by analyzing the relationship between the user track and point-of-interest (POI). This study uses regional relevance analysis and human mobility construction to establish the feature values of retail store recommendation. This study proposes (1) architecture of the data model available for retail store recommendation by influential layers of LBS; (2) System-based solution for recommendation of retail stores, adopts the influential factors with specified data in LBS and filtered by industrial types; (3) Industry density, area categories and region/industry clustering methods of POIs. Uses KDE and KMeans to calculate the effect of regional functionality on the retail store selection, similarity is used to calculate the industry category relation, and consumption capacity is considered to state saturation feature.
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U2 - 10.1007/978-3-319-62701-4_6
DO - 10.1007/978-3-319-62701-4_6
M3 - Conference contribution
AN - SCOPUS:85025140252
SN - 9783319627007
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 68
EP - 77
BT - Advances in Data Mining
A2 - Perner, Petra
PB - Springer Verlag
T2 - 17th Industrial Conference on Advances in Data Mining, ICDM 2017
Y2 - 12 July 2017 through 13 July 2017
ER -