Estimating potential customers anywhere and anytime based on location-based social networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

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 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.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015
EditorsVitor Santos Costa, Carlos Soares, Annalisa Appice, Annalisa Appice, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, João Gama, Alípio Jorge, Pedro Pereira Rodrigues, João Gama, Vitor Santos Costa, Alípio Jorge, Annalisa Appice, Pedro Pereira Rodrigues, João Gama, Annalisa Appice, Carlos Soares, Alípio Jorge, João Gama, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, Alípio Jorge
PublisherSpringer Verlag
Pages576-592
Number of pages17
ISBN (Print)9783319235240, 9783319235240, 9783319235240, 9783319235240
DOIs
Publication statusPublished - 2015 Jan 1
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 - Porto, Portugal
Duration: 2015 Sep 72015 Sep 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9285
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015
CountryPortugal
CityPorto
Period15-09-0715-09-11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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