AdCell: ad allocation in cellular networks

  • Authors:

  • Saeed Alaei;Mohammad T. Hajiaghayi;Vahid Liaghat;Dan Pei;Barna Saha

  • Affiliations:

  • University of Maryland, College Park, MD;University of Maryland, College Park, MD and AT&T Labs - Research, Florham Park, NJ;University of Maryland, College Park, MD;AT&T Labs - Research, Florham Park, NJ;University of Maryland, College Park, MD

  • Venue:
  • ESA'11 Proceedings of the 19th European conference on Algorithms
  • Year:
  • 2011

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Abstract

With more than four billion usage of cellular phones worldwide, mobile advertising has become an attractive alternative to online advertisements. In this paper, we propose a new targeted advertising policy for Wireless Service Providers (WSPs) via SMS or MMS- namely AdCell. In our model, a WSP charges the advertisers for showing their ads. Each advertiser has a valuation for specific types of customers in various times and locations and has a limit on the maximum available budget. Each query is in the form of time and location and is associated with one individual customer. In order to achieve a non-intrusive delivery, only a limited number of ads can be sent to each customer. Recently, new services have been introduced that offer location-based advertising over cellular network that fit in our model (e.g., ShopAlerts by AT&T). We consider both online and offline version of the AdCell problem and develop approximation algorithms with constant competitive ratio. For the online version, we assume that the appearances of the queries follow a stochastic distribution and thus consider a Bayesian setting. Furthermore, queries may come from different distributions on different times. This model generalizes several previous advertising models such as online secretary problem [10], online bipartite matching [13,7] and AdWords [18]. Since our problem generalizes the well-known secretary problem, no non-trivial approximation can be guaranteed in the online setting without stochastic assumptions. We propose an online algorithm that is simple, intuitive and easily implementable in practice. It is based on pre-computing a fractional solution for the expected scenario and relies on a novel use of dynamic programming to compute the conditional expectations. We give tight lower bounds on the approximability of some variants of the problem as well. In the offline setting, where full-information is available, we achieve nearoptimal bounds, matching the integrality gap of the considered linear program. We believe that our proposed solutions can be used for other advertising settings where personalized advertisement is critical.