Abstract
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 the number 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.
Chapter PDF
Similar content being viewed by others
References
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (2008)
Chen, Z., Liu, Y., Wong, R.C.-W., Xiong, J., Mai, G., Long, C.: Efficient algorithms for optimal location queries in road networks. In: ACM SIGMOD (2014)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: ACM KDD (2011)
Donald, S.: A two-dimensional interpolation function for irregularly-spaced data. In: ACM National Conference (1968)
Fu, Y., Ge, Y., Zheng, Y., Yao, Z., Liu, Y., Xiong, H., Yuan, N.J.: Sparse real estate ranking with online user reviews and offline moving behaviors. In: IEEE ICDM (2014)
Hsieh, H.-P., Lin, S.-D., Zheng, Y.: Inferring air quality for station location recommendation based on urban big data. In: ACM KDD (2015)
Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM TOIS (2002)
Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., Mascolo, C.: Geo-spotting: mining online location-based services for optimal retail store placement. In: ACM KDD (2013)
Kisilevich, S., Mansmann, F., Keim, D.: P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In: COM.Geo (2010)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML (2001)
Li, Y., Steiner, M., Wang, L., Zhang, Z.-L., Bao, J.: Exploring venue popularity in four-square. In: IEEE INFOCOM (2013)
Lima, A., Musolesi, M.: Spatial dissemination metrics for location-based social networks. In: ACM UbiComp (2012)
Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood characteristics for location recommendation. In: ACM CIKM (2014)
Mehaffy, M., Porta, S., Rofe, Y., Salingaros, N.: Urban nuclei and the geometry of streets: The emergent neighborhoods’ model. Urban Design International (2010)
Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: ACM CIKM (2000)
Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: Where next: a location predictor on trajectory pattern mining. In: ACM KDD (2009)
Oliver, M.A., Webster, R.: Kriging: a method of interpolation for geographical information systems. IJGIS (1990)
Sadilek, A., Kautz, H., Bigham, J.P.: Finding your friends and following them to where you are. In: ACM WSDM (2012)
Scellato, S., Mascolo, C., Musolesi, M., Latora, V.: Distance matters: geo-social metrics for online social networks. In: WOSN (2010)
Tiwari, S., Kaushik, S.: User category based estimation of location popularity using the road GPS trajectory databases. Geoinformatica (2014)
Ying, J.-C., Lee, W.-C., Weng, T.-C., Tseng, V.S.: Semantic trajectory mining for location prediction. In: ACM SIGSPATIAL GIS (2011)
Zhang, C., Shou, L., Chen, K., Chen, G., Bei, Y.: Evaluating geo-social influence in location-based social networks. In: ACM CIKM (2012)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: ICML (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hsieh, HP., Li, CT., Lin, SD. (2015). Estimating Potential Customers Anywhere and Anytime Based on Location-Based Social Networks. In: Appice, A., Rodrigues, P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9285. Springer, Cham. https://doi.org/10.1007/978-3-319-23525-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-23525-7_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23524-0
Online ISBN: 978-3-319-23525-7
eBook Packages: Computer ScienceComputer Science (R0)