Abstract
Sponsored search auction is used by most search engines to select ads to display on the web page of a search result, according to advertisers’ bidding prices. The income of this targeted advertising business is a big part of the revenue of most search engines. The most widely used approach to choose ads is the Generalized Second Price (GSP) auction, choosing the i-th highest bidder to display at the i-th most favorable position and charging the (i + 1)-st highest bidding price. Most previous works about GSP auction are based on the separation assumption: the probability a user will click on an ad is composed of two independent parts: a quality factor of the ad itself and a position factor of the slot of the ad. The previous model does not include the externality an ad may bring to the other ads. We study a GSP auction in a Markovian user model where the externality is considered by modeling a user’s probability behavior when he reads ad list. In particular, we propose a new ranking scheme for the bidders. We prove Nash equilibrium always exists in the auction and study the efficiency of the auction by theoretical analysis and simulation. We compare our results with social optimum and previous approaches. Comparison shows that our scheme approximates the social optimum and improves previous approaches under various circumstances.
Work reported in this article is supported by a research grant (Project No: CityU 112909) of Research Grants Council of Hong Kong SAR.
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Deng, X., Yu, J. (2009). A New Ranking Scheme of the GSP Mechanism with Markovian Users . In: Leonardi, S. (eds) Internet and Network Economics. WINE 2009. Lecture Notes in Computer Science, vol 5929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10841-9_59
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DOI: https://doi.org/10.1007/978-3-642-10841-9_59
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