A Knapsack-Based Approach to Bidding in Ad Auctions

Authors:
Jordan Berg;Amy Greenwald;Victor Naroditskiy;Eric Sodomka
Affiliations:
Brown University, USA/ {jberg, amy, sodomka}@cs.brown.edu;Brown University, USA/ {jberg, amy, sodomka}@cs.brown.edu;University of Southampton, UK/ vnarodit@gmail.com;Brown University, USA/ {jberg, amy, sodomka}@cs.brown.edu
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

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Abstract

We model the problem of bidding in ad auctions as a penalized multiple choice knapsack problem (PMCKP), a combination of the multiple choice knapsack problem (MCKP) and the penalized knapsack problem (PKP) [1]. We present two versions of PMCKPGlobalPMCKP and LocalPMCKP, together with a greedy algorithm that solves the linear relaxation of a GlobalPMCKP optimally. We also develop a greedy heuristic for solving LocalPMCKP. Although our heuristic is not optimal, we show that it performs well in TAC AA games.