Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by the daily-deal providers. Unlike the traditional web ads, the advertiser's profits for group-buying ads depends on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts, and dynamically vary bids to maximize profits. Further, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real time ad selection, admissibility, and real time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection and admission strategies on a multi-million click stream of 935 ads. The proposed real-time bidding, selection and admissibility show significant profit increases over the existing strategies. Further the experiments illustrate the robustness of the bidding and acceptable computation timings.
Different models of group-buying auctions and bidding mechanisms has been studied @cite_3 @cite_6 . But our problem of bidding to sell deals online---mostly made popular after the emergence of dail-deal sites---has not been studied for any of the group-buying auction models.
@cite_6 @cite_3
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The group-buying auction is a new kind of dynamic pricing mechanism on the Internet. It is a variant of the sellers' price double auction, which makes the bidders as a group through Internet to get the volume discounts, i.e., the more bidders bid, the lower the price of the object being auctioned becomes. In this paper, we analyze the group-buying auction under some assumptions, such as that the independent private values (IPVs) model applies and bidders are risk neutral and symmetric, etc., and build an incomplete information dynamic game model to illustrate the bidders' bidding process. It proves that for the bidders there exists a weakly dominant strategy S, i.e., no matter when a bidder arrives at the auction and what the bidding history is, the highest permitted bid price that is no greater than his value to the object is always his optimal bid price but may not be the unique one. Online auctions have become a quite popular and effective approach in the Internet-based eMarketplace. In concurrent auctions, where multiple auctions for identical items are running simultaneously, users’ bidding behaviors become very complicated. This situation motivates shilling behaviors, in which a seller disguises himself as normal bidders in order to drive up the bidding price and make the winning bidder pay more for an auctioned item. The goal of this paper is to propose a formal approach to verifying bidding behaviors, and especially, detecting shilling behaviors in concurrent online auctions. We develop a model template for concurrent auctions and derive auction models based on auction data from two concurrent auctions. The auction model can be formally verified using the SPIN model checker for certain behavioral properties, which are specified in pattern-based LTL (Linear Temporal Logic) formulas. To illustrate the feasibility and effectiveness of our approach, we provide a case study to show how possible shill bidders can be detected. We consider the problem of a single seller repeatedly selling a single item to a single buyer (specifically, the buyer has a value drawn fresh from known distribution D in every round). Prior work assumes that the buyer is fully rational and will perfectly reason about how their bids today affect the seller's decisions tomorrow. In this work we initiate a different direction: the buyer simply runs a no-regret learning algorithm over possible bids. We provide a fairly complete characterization of optimal auctions for the seller in this domain. Specifically: - If the buyer bids according to EXP3 (or any "mean-based" learning algorithm), then the seller can extract expected revenue arbitrarily close to the expected welfare. This auction is independent of the buyer's valuation D , but somewhat unnatural as it is sometimes in the buyer's interest to overbid. - There exists a learning algorithm A such that if the buyer bids according to A then the optimal strategy for the seller is simply to post the Myerson reserve for D every round. - If the buyer bids according to EXP3 (or any "mean-based" learning algorithm), but the seller is restricted to "natural" auction formats where overbidding is dominated (e.g. Generalized First-Price or Generalized Second-Price), then the optimal strategy for the seller is a pay-your-bid format with decreasing reserves over time. Moreover, the seller's optimal achievable revenue is characterized by a linear program, and can be unboundedly better than the best truthful auction yet simultaneously unboundedly worse than the expected welfare. Recent work on online auctions for digital goods has explored the role of optimal stopping theory -- particularly secretary problems -- in the design of approximately optimal online mechanisms. This work generally assumes that the size of the market (number of bidders) is known a priori, but that the mechanism designer has no knowledge of the distribution of bid values. However, in many real-world applications (such as online ticket sales), the opposite is true: the seller has distributional knowledge of the bid values (e.g., via the history of past transactions in the market), but there is uncertainty about market size. Adopting the perspective of automated mechanism design, introduced by Conitzer and Sandholm, we develop algorithms that compute an optimal, or approximately optimal, online auction mechanism given access to this distributional knowledge. Our main results are twofold. First, we show that when the seller does not know the market size, no constant-approximation to the optimum efficiency or revenue is achievable in the worst case, even under the very strong assumption that bid values are i.i.d. samples from a distribution known to the seller. Second, we show that when the seller has distributional knowledge of the market size as well as the bid values, one can do well in several senses. Perhaps most interestingly, by combining dynamic programming with prophet inequalities (a technique from optimal stopping theory) we are able to design and analyze online mechanisms which are temporally strategyproof (even with respect to arrival and departure times) and approximately efficiency (revenue)-maximizing. In exploring the interplay between automated mechanism design and prophet inequalities, we prove new prophet inequalities motivated by the auction setting.