Advertisement

Temporal popularity prediction of locations for geographical placement of retail stores

  • Hsun-Ping Hsieh
  • Fandel Lin
  • Cheng-Te LiEmail author
  • Ian En-Hsu Yen
  • Hsin-Yu Chen
Regular Paper
  • 20 Downloads

Abstract

Modern retail chains, such as Starbucks and McDonald’s, seek for geographical locations that have higher possibility to bring the maximum profit to establish and open their new stores. For a retail store, the common indicator of profit is its popularity which can be measured by the number of people who had ever consumed there. If one can know the potential popularity of locations along the time or within a certain time period (e.g., Christmas holiday or some memorial days) in advance, it would facilitate the determination of locations that are more profitable for the geographical placement of new retail stores. In this paper, given a targeted retail chain as well as some candidate locations, we propose to predict the popularity values of these locations over time to support their decisions on placing new stores. We devise a semi-supervised learning model, Affinity-based Popularity Inference, to tackle the Temporal Location Popularity Prediction problem. The basic idea is that locations that share similar properties of geo-spatial venue characteristics and human mobility tend to have closer popularity values. Experiments conducted on Foursquare check-in data in New York City exhibit promising results on predicting the temporal popularity of retail chains Starbucks, McDonald’s, and Dunkin’ Donuts, comparing to interpolation, regression, and state-of-the-art supervised learning methods.

Keywords

Location popularity Popularity prediction Retail store placement Location-based services Spatiotemporal data mining 

Notes

Acknowledgements

This work was sponsored by Ministry of Science and Technology (MOST) of Taiwan under Grants 107-2221-E-006-199, 106-2118-M-006-010-MY2, 106-2221-E-006-221, 107-2636-E-006-002, and 107-2218-E-006-040 and also by Academia Sinica under Grant AS-107-TP-A05.

References

  1. 1.
    Chen Z, Liu Y, Wong RC-W, Xiong J, Mai G, Long C (2014) Efficient algorithms for optimal location queries in road networks. In: ACM SIGMOD international conference on management of data (SIGMOD), pp 123–134Google Scholar
  2. 2.
    Donald S (1968) A two-dimensional interpolation function for irregularly-spaced data. In: ACM national conference, pp 517–524Google Scholar
  3. 3.
    Fu Y, Ge Y, Zheng Y, Yao Z, Liu Y, Xiong H, Yuan NJ (2014) Sparse real estate ranking with online user reviews and offline moving behaviors. In: IEEE international conference on data mining (ICDM), pp 120–129Google Scholar
  4. 4.
    Karamshuk D, Noulas A, Scellato S, Nicosia V, Mascolo C (2013) Geo-spotting: mining online location-based services for optimal retail store placement. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 791–801Google Scholar
  5. 5.
    Kisilevich S, Mansmann F, Keim D (2010) P-dbscan: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In: International conference and exhibition on computing for geospatial research and application (COM.Geo)Google Scholar
  6. 6.
    Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: International conference on machine learning (ICML), pp 282–289Google Scholar
  7. 7.
    Li Y, Steiner M, Wang L, Zhang Z-L, Bao J (2012) Dissecting foursquare venue popularity via random region sampling. In: ACM conference on CoNEXT student workshop, pp 21–22Google Scholar
  8. 8.
    Li Y, Steiner M, Wang L, Zhang Z-L, Bao J (2013) Exploring venue popularity in foursquare. In: IEEE international conference on computer communications (INFOCOM), pp 3357–3362Google Scholar
  9. 9.
    Liu B, Fu Y, Yao Z, Xiong H (2013) Learning geographical preferences for point-of-interest recommendation. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 1043–1051Google Scholar
  10. 10.
    Liu X, Liu Y, Aberer K, Miao C (2013) Personalized point-of-interest recommendation by mining users’ preference transition. In: ACM international conference on information and knowledge management (CIKM), pp 733–738Google Scholar
  11. 11.
    Liu Y, Liu C, Liu B, Qu M, Xiong H (2016) Unified point-of-interest recommendation with temporal interval assessment. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’16, pp 1015–1024Google Scholar
  12. 12.
    Liu Y, Liu C, Lu X, Teng M, Zhu H, Xiong H (2017) Point-of-interest demand modeling with human mobility patterns. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 947–955Google Scholar
  13. 13.
    Liu Y, Wei W, Sun A, Miao C (2014) Exploiting geographical neighborhood characteristics for location recommendation. In: ACM international conference on information and knowledge management (CIKM), pp 739–748Google Scholar
  14. 14.
    Mehaffy M, Porta S, Rofe Y, Salingaros N (2010) Urban nuclei and the geometry of streets: the emergent neighborhoods’ model. Urban Design Int 15(1):22–46CrossRefGoogle Scholar
  15. 15.
    Nigam K, Ghani R (2000) Analyzing the effectiveness and applicability of co-training. In: ACM international conference on information and knowledge management (CIKM), pp 86–93Google Scholar
  16. 16.
    Noulas A, Scellato S, Lathia N, Masolo C (2012) Mining user mobility features for next place prediction in location-based services. In: IEEE international conference on data mining (ICDM), pp 1038–1043Google Scholar
  17. 17.
    Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Syst 4(3):313–332CrossRefGoogle Scholar
  18. 18.
    Quan X, Wenyin L, Dou W, Xiong H, Ge Y (2012) Link graph analysis for business site selection. Computer 45(3):64–69CrossRefGoogle Scholar
  19. 19.
    Tiwari S, Kaushik S (2014) User category based estimation of location popularity using the road GPS trajectory databases. Geoinform Int J 4(2):20–31Google Scholar
  20. 20.
    Yao Z, Fu Y, Liu B, Liu Y, Xiong H (2016) Poi recommendation: a temporal matching between poi popularity and user regularity. In: 2016 IEEE 16th international conference on data mining (ICDM), pp 549–558Google Scholar
  21. 21.
    Ye M, Yin P, Lee W-C, Lee D-L (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: ACM international conference on research and development in information retrieval (SIGIR), pp 325–334Google Scholar
  22. 22.
    Yuan NJ, Zheng Y, Xie X, Wang Y, Zheng K, Xiong H (2015) Discovering urban functional zones using latent activity trajectories. IEEE Trans Knowl Data Eng 27(3):712–725CrossRefGoogle Scholar
  23. 23.
    Yuan Q, Cong G, Ma Z, Sun A, Magnenat-Thalmann N (2013) Time-aware point-of-interest recommendation. In: ACM international conference on research and development in information retrieval (SIGIR), pp 363–372Google Scholar
  24. 24.
    Zhang C, Shou L, Chen K, Chen G, Bei Y (2012) Evaluating geo-social influence in location-based social networks. In: ACM international conference on information and knowledge management (CIKM), pp 1442–1451Google Scholar
  25. 25.
    Zheng Y, Liu F, Hsieh H-P (2013) U-air: When urban air quality inference meets big data. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 1436–1444Google Scholar
  26. 26.
    Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using Gaussian fields and harmonic functions. In: International conference on machine learning (ICML), pp 912–919Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Institute of Computer and Communication EngineeringNational Cheng Kung UniversityTainanTaiwan
  2. 2.Department of Electrical EngineeringNational Cheng Kung UniversityTainanTaiwan
  3. 3.Department of Statistics, Institute of Data ScienceNational Cheng Kung UniversityTainanTaiwan
  4. 4.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA
  5. 5.Department of StatisticsNational Cheng Kung UniversityTainanTaiwan

Personalised recommendations