Contents

Assessing Association of Dissimilarity of Business Categories in a Commercial Area with Merchants’ Revenue

Bahrami, Mohsen / Yoon, Jisung / Bozkaya, Burcin / Balcisoy, Selim / Jung, Woo-Sung / Pentland, Alex / Ahn, Yong-Yeol

  • 7109 ITEM VIEW
  • 0 DOWNLOAD
Abstract

In this study, we assess the diversity of commercial districts with representation learning approaches leveraging large-scale geo-tagged credit card transactions data.We show that the homogeneity of a commercial district and the merchants’ revenue have an inverted U-shape (concave) relationship.
Our study results suggest that if the merchants in a commercial district are too similar or too different, the revenue is likely to decrease, but there exists an optimal point as a result of a concave relationship. Additionally, we show that if the structure is more concise (measured by the standard deviation of pairwise similarity), then the revenue is likely to increase.

Issue Date
2022-10-17
Publisher
Institute for Operations Research and the Management Sciences
URI
https://archives.kdischool.ac.kr/handle/11125/54655
URL
https://www.abstractsonline.com/pp8/?__hstc=194041586.36817b17c200fd1270a899c841c5939f.1719296625389.1719296625389.1719296625389.1&__hssc=194041586.1.1719296625389&__hsfp=607159395&hsCtaTracking=19b73a50-cb7c-44d9-a0fa-7d986afa8569%7Cb47fb993-b3f8-4747-b21d-ad3b7777ce3a#!/10693/presentation/10268
Conf. Name
2022 Informs Annual Meeting
Place
US
Conference Date
2022-10-16
Files in This Item:
    There are no files associated with this item.

Click the button and follow the links to connect to the full text. (KDI CL members only)

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

상단으로 이동