Generalized second price auction with probabilistic broad match

Generalized second price auction with probabilistic broad match
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概率广泛匹配的广义第二价格拍卖

DOI:
10.1145/2600057.2602828
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发表时间:
2014
期刊:
Proceedings of the fifteenth ACM conference on Economics and computation
影响因子:
--
通讯作者:
L. Wang
L. Wang
中科院分区:
--
文献类型:
--
作者:
W. Chen;T.-Y. Liu;T. Qin;Y. Tao;L. Wang

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通用第二价格(GSP)拍卖被广泛使用的搜索引擎今天出售他们的广告位。大多数搜索引擎在执行GSP拍卖时已经支持查询和出价关键字之间的广泛匹配,然而,已经揭示的是,具有它们当前使用的标准广泛匹配机制(表示为SBM-GSP)的GSP拍卖具有几个理论上的缺点(例如,它的理论特性仅在单时隙情况和完全信息设置下是已知的,并且即使在这种简单设置下,相应的最坏情况的社会福利也可能相当差)。为了解决这个问题,我们提出了一种新的宽匹配机制,我们称之为概率宽匹配(PBM)机制。与SBM不同,SBM将所有与给定查询匹配的关键字上的广告放在一起用于GSP拍卖,具有PBM的GSP(表示为PBM-GSP)根据预定义的概率分布随机采样关键字,并且仅对该采样关键字上的广告进行GSP拍卖。我们对PBM-GSP的理论性质进行了全面的研究。具体来说,我们研究了它的社会福利在最坏的均衡,在完全信息和贝叶斯设置。结果表明,PBM-GSP比SBM-GSP在温和的条件下产生更大的福利。此外,我们还研究了贝叶斯环境下PBM-GSP的收益保证问题。据我们所知,这是第一个关于普惠制广泛匹配机制的工作,它超越了单槽情况和全信息设置。
Generalized Second Price (GSP) auctions are widely used by search engines today to sell their ad slots. Most search engines have supported the broad match between queries and bid keywords when executing the GSP auctions, however, it has been revealed that the GSP auction with the standard broad-match mechanism they are currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g., its theoretical properties are known only for the single-slot case and full-information setting, and even in this simple setting, the corresponding worst-case social welfare can be rather bad). To address this issue, we propose a novel broad-match mechanism, which we call theProbabilistic Broad-Match(PBM) mechanism. Different from SBM that puts together the ads bidding on all the keywords matched to a given query for the GSP auction, the GSP with PBM (denoted as PBM-GSP) randomly samples a keyword according to a predefined probability distribution and only runs the GSP auction for the ads bidding on this sampled keyword. We perform a comprehensive study on the theoretical properties of the PBM-GSP. Specifically, we study its social welfare in the worst equilibrium, in both full-information and Bayesian settings. The results show that PBM-GSP can generate larger welfare than SBM-GSP} under mild conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in Bayesian setting. To the best of our knowledge, this is the first work on broad-match mechanisms for GSP that goes beyond the single-slot case and the full-information setting.
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