A Statistical Approach to Inferring Business Locations Based on Purchase Behavior

Yehezkel S. Resheff; Moni Shahar

doi:10.1109/BigData.2018.8622134

A Statistical Approach to Inferring Business Locations Based on Purchase Behavior

Yehezkel S. Resheff, Moni Shahar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
Editors	Naoki 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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2295-2303
Number of pages	9
ISBN (Electronic)	9781538650356
DOIs	https://doi.org/10.1109/BigData.2018.8622134
State	Published - 2 Jul 2018
Externally published	Yes
Event	2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: 10 Dec 2018 → 13 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference	2018 IEEE International Conference on Big Data, Big Data 2018
Country/Territory	United States
City	Seattle
Period	10/12/18 → 13/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Access to Document

10.1109/BigData.2018.8622134

Cite this

Resheff, Y. S., & Shahar, M. (2018). A Statistical Approach to Inferring Business Locations Based on Purchase Behavior. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, & J. Saltz (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 2295-2303). Article 8622134 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622134

Resheff, Yehezkel S. ; Shahar, Moni. / A Statistical Approach to Inferring Business Locations Based on Purchase Behavior. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Naoki 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. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2295-2303 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

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title = "A Statistical Approach to Inferring Business Locations Based on Purchase Behavior",

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.",

author = "Resheff, {Yehezkel S.} and Moni Shahar",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",

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doi = "10.1109/BigData.2018.8622134",

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Resheff, YS & Shahar, M 2018, A Statistical Approach to Inferring Business Locations Based on Purchase Behavior. in N Abe, H Liu, C Pu, X Hu, N Ahmed, M Qiao, Y Song, D Kossmann, B Liu, K Lee, J Tang, J He & J Saltz (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622134, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2295-2303, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 10/12/18. https://doi.org/10.1109/BigData.2018.8622134

A Statistical Approach to Inferring Business Locations Based on Purchase Behavior. / Resheff, Yehezkel S.; Shahar, Moni.
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Naoki 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. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2295-2303 8622134 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Resheff YS, Shahar M. A Statistical Approach to Inferring Business Locations Based on Purchase Behavior. In Abe N, Liu H, Pu C, Hu X, Ahmed N, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz J, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2295-2303. 8622134. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). doi: 10.1109/BigData.2018.8622134

A Statistical Approach to Inferring Business Locations Based on Purchase Behavior

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