A Statistical Approach to Inferring Business Locations Based on Purchase Behavior

Yehezkel S. Resheff, Moni Shahar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Transaction data obtained by Personal Financial Management (PFM) services from financial institutes such as banks and credit card companies contain a description string from which the merchant identity and an encoded store identifier may be parsed. However, the physical location of the purchase is absent from this description. In this paper we present a method designed to recover this valuable spatial information and map merchant and identifier tuples to physical map locations. We begin by constructing a graph of customer sharing between businesses, and based on a small set of known »seed» locations we formulate this task as a maximum likelihood problem using a model of customer sharing between nearby businesses. We test our method extensively on real world data and provide statistics on the displacement error in many cities.

Original languageAmerican English
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsNaoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2295-2303
Number of pages9
ISBN (Electronic)9781538650356
DOIs
StatePublished - 2 Jul 2018
Externally publishedYes
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: 10 Dec 201813 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
Country/TerritoryUnited States
CitySeattle
Period10/12/1813/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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