Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design

Foster Provost (), David Martens () and Alan Murray ()
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Foster Provost: Department of Information, Operations and Management Sciences, Leonard N. Stern School of Business, New York University, New York, New York 10012
David Martens: Department of Engineering Management, Faculty of Applied Economics, University of Antwerp, B-2000 Antwerp, Belgium
Alan Murray: Coriolis Labs, New York, New York 10021

Information Systems Research, 2015, vol. 26, issue 2, 243-265

Abstract: This paper focuses on finding the same and similar users based on location-visitation data in a mobile environment. We propose a new design that uses consumer-location data from mobile devices (smartphones, smart pads, laptops, etc.) to build a “geosimilarity network” among users. The geosimilarity network (GSN) could be used for a variety of analytics-driven applications, such as targeting advertisements to the same user on different devices or to users with similar tastes, and to improve online interactions by selecting users with similar tastes. The basic idea is that two devices are similar, and thereby connected in the GSN, when they share at least one visited location. They are more similar as they visit more shared locations and as the locations they share are visited by fewer people. This paper first introduces the main ideas and ties them to theory and related work. It next introduces a specific design for selecting entities with similar location distributions, the results of which are shown using real mobile location data across seven ad exchanges. We focus on two high-level questions: (1) Does geosimilarity allow us to find different entities corresponding to the same individual, for example, as seen through different bidding systems? And (2) do entities linked by similarities in local mobile behavior show similar interests, as measured by visits to particular publishers? The results show positive results for both. Specifically, for (1), even with the data sample’s limited observability, 70%–80% of the time the same individual is connected to herself in the GSN. For (2), the GSN neighbors of visitors to a wide variety of publishers are substantially more likely also to visit those same publishers. Highly similar GSN neighbors show very substantial lift.

Keywords: design science; mobile computing; analytical modeling; network analysis (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:26:y:2015:i:2:p:243-265

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