搜索引擎的广告拍卖平台设计与开发

搜索引擎的是重要的广告媒体之一,开发搜索引擎的广告拍卖平台有利于广告资源的有效利用。本文设计了一个搜索引擎的广告拍卖平台,支持广告用户的自行报价,并在收集竞价信息的基础上实现了广告位的分配与定价;然后,根据实际应用背景划分了系统的功能模块并定义了拍卖的业务流程;最后,基于MyEclipse6.0 开发工具和MySQL后台数据库实现了该系统。     

基于淘宝直通车的竞价决策系统的研究与设计

电子商务蓬勃发展的大环境下,广告主具有强烈的电商广告投放意愿,显然他们并未达到电商广告的核心业务SEM （搜索引擎营销优化）的专业要求。所以广告主希望借助第三方工具来进行搜索引擎广告投放的一站式服务来满足其业务需求。基于此,本文将提供一整套的竞价词托管式服务的解决方案。以淘宝直通车这一全新的搜索竞价模式作为研究对象,从语义抽取、关键词扩展、竞价词生成、模型化出价、广告效果正向反馈监控模型几方面进行分析和统计,为直通车广告主提供最优投放策略整体解决方案。第一阶段针对商品信息进行数据挖掘,实现关键词推荐引擎。第二阶段实现投放优化模块,实施定价策略,建立的点击量与PPC（“平均点击花费”）模型,实现在预算资金的约束下对不同竞价组合进行ROI（投入产出比）最大化的投资决策。以实际效果改善直通车竞价搜索用户体验。     

A dynamic model of bidding patterns in sponsored search auctions

We develop an infinite horizon alternative-move model of the unique second-price sponsored search auction. We use this model to explain two distinguishable bidding patterns observed in our bidding data: bidding war cycle and stable bid. With examples, we show that only a small portion of the value generated may be extracted by search engines, if advertisers are engaged in bidding war cycles. Finally, we show the impact of auction design on advertiser bids and search engine revenue.     

Optimal reserve prices in weighted GSP auctions

Most search engines use the weighted Generalized Second Price (wGSP) auction to sell keyword-based text ads, generating billions of dollars of advertising revenue every year. Designing and implementing near-optimal reserve prices for these wGSP auctions are naturally important problems for both academia and industry.In this paper, we show how to calculate and implement the near-optimal reserve price of the wGSP mechanism in realistic settings. Unlike reserve prices in standard single-item auctions, optimal reserve prices in wGSP auctions are discriminatory, different even for advertisers bidding on the same keyword. The optimal reserve price results can be extended to support CPA/CPC/CPM¹ hybrid auctions.Our simulations indicate that setting a proper reserve price will transfer some bidder utility (payoff) to auctioneer utility, resulting in higher revenue for the search engine. We describe a practical methodology to implement optimal reserve prices in production systems.     

An Optimal Mechanism for Sponsored Search Auctions on the Web and Comparison With Other Mechanisms

In this paper, we first describe a framework to model the sponsored search auction on the Web as a mechanism design problem. Using this framework, we describe two well-known mechanisms for sponsored search auction - generalized second price (GSP) and Vickrey-Clarke-Groves (VCG). We then derive a new mechanism for sponsored search auction which we call optimal (OPT) mechanism. The OPT mechanism maximizes the search engine's expected revenue, while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We then undertake a detailed comparative study of the mechanisms GSP, VCG, and OPT. We compute and compare the expected revenue earned by the search engine under the three mechanisms when the advertisers are symmetric and some special conditions are satisfied. We also compare the three mechanisms in terms of incentive compatibility, individual rationality, and computational complexity.     

Sponsored search auctions: an overview of research with emphasis on game theoretic aspects

We provide a broad overview of the research that has been conducted until recently on the design of sponsored search auctions. We mainly focus on game theoretic and mechanism design aspects of these auctions, and we analyze the issues associated with each of the three participating entities, i.e., the search engine, the advertisers, and the users of the search engine, as well as their resulting behavior. Regarding the search engine, we overview the various mechanisms that have been proposed including the currently used GSP mechanism. The issues that are addressed include analysis of Nash equilibria and their performance, design of alternative mechanisms and aspects of competition among search engines. We then move on to the advertisers and discuss the problem of choosing a bidding strategy, given the mechanism of the search engine. Following this, we consider the end users and we examine how user behavior may create externalities and influence the performance of the advertisers. Finally, we also overview statistical methods for estimating modeling parameters that are of interest to the three entities. In each section, we point out interesting open problems and directions for future research.     

基于带惩罚的点概率距离策略优化算法在展示广告实时竞标中的研究

实时竞价(RTB)是在线展示广告中被广泛采用的广告投放模式,针对由于RTB拍卖环境的高度动态性导致最佳出价策略难以获得的问题,提出了一种基于强化学习(RL)的出价策略优化方法,即采用带惩罚的点概率距离策略优化(POP3D)算法来学习最佳出价策略。在基于POP3D的出价框架中,广告投标过程被建模为情节式的马尔可夫决策过程,每个情节被划分为固定数量的时间步,每个广告展示的出价由它的预估点击率大小和竞标因子共同决定。每个时间步,竞标代理都会根据上一时间步的拍卖情况对竞标因子进行调整,以使得出价策略能够适应高度动态的拍卖环境,竞标代理的目标是学习最佳的竞标因子调整策略。在iPinYou数据集上的实验结果表明,与DRLB算法相比,所提出价算法在预算比例为1/16和1/32时,在点击次数方面均提升了0.2%;当预算比例为1/8、1/16和1/32时,在赢标率方面分别提升了1.8%、1.0%和1.7%;另外,在稳定性方面,所提方法也具有优势。表明了该方法的优越性。     

MoCAAS: auction agent system using a collaborative mobile agent in electronic commerce

To get the items that a buyer wants in an Internet auction, he must search for the items through several auction sites. When the bidding starts, the buyer needs to connect to these auction sites frequently so that he can monitor the bid states and re-bid. A reserve-price auction reduces the number of connections, but this limits the user's bidding strategy. Another problem is equity between the buyer and the seller. Both the buyer and the seller should profit within proper limits. In this paper, we propose an auction agent system using a collaborative mobile agent and a brokering mechanism called MoCAAS (Mobile collaborative auction agent system), which mediates between the buyer and the seller and executes bidding asynchronously and autonomously. This reduces the network load more than with other auction-agents, offers more intelligent bidding, and increases the clear ratio.     

Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market

A novel stochastic optimization approach to solve optimal bidding strategy problem in a pool based electricity market using fuzzy adaptive gravitational search algorithm (FAGSA) is presented. Generating companies (suppliers) participate in the bidding process in order to maximize their profits in an electricity market. Each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. The gravitational search algorithm (GSA) is tedious to solve the optimal bidding strategy problem because, the optimum selection of gravitational constant (G). To overcome this problem, FAGSA is applied for the first time to tune the gravitational constant using fuzzy “IF/THEN” rules. The fuzzy rule-based systems are natural candidates to design gravitational constant, because they provide a way to develop decision mechanism based on specific nature of search regions, transitions between their boundaries and completely dependent on the problem. The proposed method is tested on IEEE 30-bus system and 75-bus Indian practical system and compared with GSA, particle swarm optimization (PSO) and genetic algorithm (GA). The results show that, fuzzification of the gravitational constant, improve search behavior, solution quality and reduced computational time compared against standard constant parameter algorithms.     

Expert systems and economic policy analysis: An application to OCS auction leasing

In this paper we argue that expert systems can be powerful tools for modelling microeconomic systems, including both individual decision making and the coordination of individual agents in a resource allocation mechanism. Using the fact that expert systems are essentially computerized versions of decision processes, we illustrate how they can be viewed as generalized process models of decision-making. We argue that the expert system approach is beneficial because it allows a policy analyst to explore the implication of policy alternatives without having to incur the generally prohibitive cost of field implementation studies. Further, enables the incorporation and updating of decision strategies and qualitative information, which human experts typically use but which is not amenable to pure mathematical modelling.One particular microeconomic system we suggest could be modelled as an expert system is the OCS offshore oil lease auction process. Moreover, we argue that constructing such an expert system model would require the development of two integrated expert systems: one for the auction process and subsequent resource allocation and the other to model the individual bidding behavior of the auction participants. We set out the structure of the auction expert system in some detail and discuss rules of thumb used by bidders inferred from our empirical research on past OCS auctions.Such an expert system of an auction leasing process could provide benefits to both bidders (e.g., oil companies) and the auctioneer (e.g., the Department of the Interior) as well. Bidders, by trying different strategies against different hypothesized strategies by their opponents could use such an integrated expert system to improve their bidding performances. The auctioneer, on the other hand, could test the efficiency of various proposed auction institutions under different assumptions about bidding behavior. In some circumstances, it might be desirable to even automate the auction process with a network coordinating the expert systems used by the individual firms and a computerized auctioneer.     

Procurement decision making mechanism of divisible goods based on multi-attribute auction

Multi-attribute auction enables negotiation on several attributes in addition to the price such as quality, quantity, time of delivery and service levels. Most of the existing multi-attribute auction mechanisms are designed by considering a unique good or indivisible multiple goods. This paper focuses on designing a multi-attribute auction mechanism for addressing the decision making problem of multi-attribute and multi-source procurement of a kind of homogeneous continuous divisible goods (such as coal, oil, electricity and gas). The suppliers’ optimal bidding strategies are discussed, and a bidding method named the minimum bid increment method is proposed to simulate the overall process of suppliers’ multiple rounds of bidding. Theoretical analysis shows that our auction mechanism is an efficient mechanism, and satisfies the incentive compatibility conditions and the individual rationality conditions. Moreover, a multi-attribute auction example about the steam coal procurement is given to show how to implement our multi-attribute auction mechanism.     

Design of coordination strategies in multiagent systems via genetic fuzzy systems

This paper suggests an evolutionary approach to design coordination strategies for multiagent systems. Emphasis is given to auction protocols since they are of utmost importance in many real world applications such as power markets. Power markets are one of the most relevant instances of multiagent systems and finding a profitable bidding strategy is a key issue to preserve system functioning and improve social welfare. Bidding strategies are modeled as fuzzy rule-based systems due to their modeling power, transparency, and ability to naturally handle imprecision in input data, an essential ingredient to a multiagent system act efficiently in practice. Specific genetic operators are suggested in this paper. Evolution of bidding strategies uncovers unknown and unexpected agent behaviors and allows a richer analysis of auction mechanisms and their role as a coordination protocol. Simulation experiments with a typical power market using actual thermal plants data show that the evolutionary, genetic-based design approach evolves strategies that enhance agents profitability when compared with the marginal cost-based strategies commonly adopted     

EFFECTS OF A RATIONING RULE ON THE AUSUBEL AUCTION: A GENETIC ALGORITHM IMPLEMENTATION

The increasing use of auctions as a selling mechanism has led to a growing interest in the subject. Thus both auction theory and experimental examinations of these theories are being developed. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this article is to develop a genetic algorithm to find automatically a bidder optimal strategy while the other players are always bidding sincerely. To this end a specific dynamic multiunit auction has been selected: the Ausubel auction, with private values, dropout information, and with several rationing rules implemented. The method provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments that differ in the elasticity of their demand curves, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform sincere bidding when rationing is needed.     

Towards real-time dynamic spectrum auctions

In this paper, we propose a low-complexity auction framework to distribute spectrum in real-time among a large number of wireless users with dynamic traffic. Our design consists of a compact and highly expressive bidding format, two pricing models to control tradeoffs between revenue and fairness, and fast auction clearing algorithms to achieve conflict-free spectrum allocations that maximize auction revenue. We develop analytical bounds on algorithm performance and complexity to verify the efficiency of the proposed approach. We also use both simulated and real deployment traces to evaluate the auction framework. We conclude that pricing models and bidding behaviors have significant impact on auction outcomes and spectrum utilization. Any efficient spectrum auction system must consider demand and spectrum availability in local regions to maximize system-wide revenue and spectrum utilization.     

A grey-box approach to automated mechanism design

This paper presents an approach to automated mechanism design in the domain of double auctions. We describe a novel parameterized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.     

A hybrid mechanism for heterogeneous e-procurement involving a combinatorial auction and bargaining

We discuss the design of a hybrid mechanism for e-procurement, which implements a multi-attribute combinatorial auction, followed by a bargaining process to achieve desirable procurement transaction outcomes. For the auction phase of the mechanism, we discuss incentive-compatible bidding strategies for suppliers, and how the buyer should determine the winning suppliers. In the follow-on bargaining phase, the buyer can implement a pricing strategy that views the winning suppliers as though they are in different groups. We develop a model and derive decision conditions for the buyer to formulate procurement strategy in this context. Our most important finding is that, compared with the classical Vickrey–Clarke–Groves mechanism, the proposed mechanism improves the transactional social surplus, by including the possibility of post-auction bargaining. We also consider the likelihood that such a hybrid mechanism will be able to provide sustainable business value so long as there is reasonable symmetry in bargaining power between the buyer and the supplier. We offer some thoughts on how to extend this research with approaches from behavioral economics and experimental methods.     

应急物资采购及代储服务交易的逆向组合拍卖机制设计

建立市场化的政企联合储备模式已经成为应急物资储备体系建设的重要方式.基于此,着眼于应急物资采购及代储服务的交易问题,设计一个逆向组合拍卖机制.在此拍卖机制中,政府是拍卖的买方兼委托人,企业是拍卖的卖方兼竞拍者,应急物资采购及代储服务是拍卖商品.首先,通过一个报童模型建立政府决策行为与拍卖活动之间的关系,并提出企业的投标策略;其次,建立最小化供需偏差和最大化供给数量的竞胜标决定模型;最后,提出一个符合实际背景的数值算例对拍卖机制进行模拟和验证.研究表明,所提出的逆向组合拍卖机制不仅具有经济效率,还能够促进政府一次性达成与多家企业在多个周期的合作.由此可见,运用拍卖机制解决应急物资政企联合储备的交易问题具备理论的优越性和现实的适用性.     

Auction-based resource allocation game under a hierarchical structure

This paper studies a class of auction-based resource allocation games under a hierarchical structure, such that each supplier is assigned a certain amount of resource from a single provider and allocates it to its buyers with auction mechanisms. To implement the efficient allocations for the underlying hierarchical system, we first design an auction mechanism, for each local system composed of a supplier and its buyers, which inherits the advantages of the progressive second price mechanism. By employing a dynamic algorithm, each local system converges to its own efficient Nash equilibrium, at which the efficient resource allocation is achieved and the bidding prices of all the buyers in this local system are identical with each other. After the local systems reach their own equilibria respectively, the resources assigned to suppliers are readjusted via a dynamic hierarchical algorithm with respect to the bidding prices associated with the implemented equilibria of local systems. By applying the proposed hierarchical process, the formulated hierarchical system can converge to the efficient allocation under certain mild conditions. The developed results in this work are demonstrated with simulations.     

Strong activity rules for iterative combinatorial auctions

Activity rules have emerged in recent years as an important aspect of practical auction design. The role of an activity rule in an iterative auction is to suppress strategic behavior by bidders and promote simple, continual, meaningful bidding and thus, price discovery. These rules find application in the design of iterative combinatorial auctions for real world scenarios, for example in spectrum auctions, in airline landing slot auctions, and in procurement auctions. We introduce the notion of strong activity rules, which allow simple, consistent bidding strategies while precluding all behaviors that cannot be rationalized in this way. We design such a rule for auctions with budget-constrained bidders, i.e., bidders with valuations for resources that are greater than their ability to pay. Such bidders are of practical importance in many market environments, and hindered from bidding in a simple and consistent way by the commonly used revealed-preference activity rule, which is too strong in such an environment. We consider issues of complexity, and provide two useful forms of information feedback to guide bidders in meeting strong activity rules. As a special case, we derive a strong activity rule for non-budget-constrained bidders. The ultimate choice of activity rule must depend, in part, on beliefs about the types of bidders likely to participate in an auction event because one cannot have a rule that is simultaneously strong for both budget-constrained bidders and quasi-linear bidders.