Toward Expressive and Scalable Sponsored Search Auctions

Internet search results are a growing and highly profitable advertising platform. Search providers auction advertising slots to advertisers on their search result pages. Due to the high volume of searches and the users' low tolerance for search result latency, it is imperative to resolve these auctions fast. Current approaches restrict the expressiveness of bids in order to achieve fast winner determination, which is the problem of allocating slots to advertisers so as to maximize the expected revenue given the advertisers' bids. The goal of our work is to permit more expressive bidding, thus allowing advertisers to achieve complex advertising goals, while still providing fast and scalable techniques for winner determination.Comment: 10 pages, 13 figures, ICDE 200

Scalable Winner Determination in Advertising Auctions

Internet search results are a growing and highly profitable advertising platform. Search providers auction advertising slots to advertisers on their search result pages. Due to the high volume of searches and the users' low tolerance for search result latency, it is imperative to resolve these auctions fast. Current approaches restrict the expressiveness of bids in order to achieve fast winner determination, which is the problem of allocating slots to advertisers so as to maximize the expected revenue given the advertisers' bids. The goal of our work is to permit more expressive bidding, thus allowing advertisers to achieve complex advertising goals, while still providing fast and scalable techniques for winner determination. We also discuss the application of our framework to advertising in massively multiplayer online games.NS

Bid Optimization in Broad-Match Ad auctions

Ad auctions in sponsored search support ``broad match'' that allows an advertiser to target a large number of queries while bidding only on a limited number. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high profit results in one bidding for related queries which may yield low or even negative profits. We abstract and study the complexity of the {\em bid optimization problem} which is to determine an advertiser's bids on a subset of keywords (possibly using broad match) so that her profit is maximized. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present an linear programming (LP)-based polynomial-time algorithm that gets the optimal profit. In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. To deal with this hardness result, we present a constant-factor approximation when the optimal profit significantly exceeds the cost. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can find two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. Our results are the first to address bid optimization under the broad match feature which is common in ad auctions.Comment: World Wide Web Conference (WWW09), 10 pages, 2 figure

Corporate influence and the academic computer science discipline. [4: CMU]

Prosopographical work on the four major centers for computer research in the United States has now been conducted, resulting in big questions about the independence of, so called, computer science

A study of matching mechanisms.

Liu, Jian.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 86-91).Abstracts in English and Chinese.Chapter 1 --- Introduction of Matching Mechanisms --- p.1Chapter 1.1 --- Background for College Admissions Problem --- p.1Chapter 1.2 --- Background for Internet Advertising Market --- p.3Chapter 2 --- Application I: College Admissions Problem Revisited --- p.6Chapter 2.1 --- Three Basic Mechanisms --- p.6Chapter 2.1.1 --- Boston Mechanism --- p.7Chapter 2.1.2 --- Gale-Shapley Student Optimal Mechanism --- p.9Chapter 2.1.3 --- Top Trading Cycles Mechanism --- p.11Chapter 2.2 --- College Admissions Mechanisms Around the World --- p.12Chapter 2.2.1 --- Serial Dictatorship in Turkey --- p.13Chapter 2.2.2 --- JUPAS in Hong'Kong SAR --- p.14Chapter 2.2.3 --- College Admissions in Mainland China --- p.16Chapter 2.3 --- Generalized Model for College Admissions: JUPAS Revisited --- p.19Chapter 2.4 --- Extension to Marriage Problem --- p.23Chapter 2.5 --- Strategy Analysis in Extended Marriage Problem --- p.27Chapter 2.6 --- Strategy Analysis in JUPAS --- p.30Chapter 2.7 --- Efficiency Investigation via Simulation --- p.33Chapter 2.7.1 --- Efficiency Definition --- p.33Chapter 2.7.2 --- Simulation Design --- p.36Chapter 2.7.3 --- Simulation Results --- p.38Chapter 3 --- Application II: Search Engines Market Model --- p.42Chapter 3.1 --- The Monopoly Market Model --- p.42Chapter 3.1.1 --- The Ex Ante Case --- p.43Chapter 3.1.2 --- The Ex Post Case --- p.45Chapter 3.1.3 --- Formulated As An Optimization Problem --- p.51Chapter 3.2 --- The Duopoly Market Model --- p.54Chapter 3.2.1 --- Competition for End Users in Stage I --- p.54Chapter 3.2.2 --- Competition for Advertisers in Stage II and III --- p.57Chapter 3.2.3 --- Comparison of Competition and Monopoly --- p.65Chapter 3.3 --- Numerical Results and Observations --- p.70Chapter 3.3.1 --- Baseline Setting --- p.71Chapter 3.3.2 --- Effect of Supplies --- p.74Chapter 3.3.3 --- Effect of Discount Factors --- p.75Chapter 4 --- Related Work --- p.78Chapter 5 --- Summary and Future Directions --- p.83Bibliography --- p.8

Getting In On the Act: How Arts Groups are Creating Opportunities for Active Participation

Arts participation is being redefined as people increasingly choose to engage with art in new, more active and expressive ways. This movement carries profound implications, and fresh opportunities, for the nonprofit arts sector.We are in the midst of a seismic shift in cultural production, moving from a "sit-back-and-be-told culture" to a "making-and-doing-culture." Active or participatory arts practices are emerging from the fringes of the Western cultural tradition to capture the collective imagination. Many forces have conspired to lead us to this point. The sustained economic downturn that began in 2008, rising ticket prices, the pervasiveness of social media, the roliferation of digital content and rising expectations for self-guided, on-demand, customized experiences have all contributed to a cultural environment primed for active arts practice. This shift calls for a new equilibrium in the arts ecology and a new generation of arts leaders ready to accept, integrate and celebrate all forms of cultural practice. This is, perhaps, the defining challenge of our time for artists, arts organizations and their supporters -- to embrace a more holistic view of the cultural ecology and identify new possibilities for Americans to engage with the arts.How can arts institutions adapt to this new environment?Is participatory practice contradictory to, or complementary to, a business model that relies on professional production and consumption?How can arts organizations enter this new territory without compromising their values r artistic ideals?This report aims to illuminate a growing body of practice around participatory engagement (with various illustrative case studies profiled at the end) and dispel some of the anxiety surrounding this sphere of activity

Practical Strategic Reasoning with Applications in Market Games.

Strategic reasoning is part of our everyday lives: we negotiate prices, bid in auctions, write contracts, and play games. We choose actions in these scenarios based on our preferences, and our beliefs about preferences of the other participants. Game theory provides a rich mathematical framework through which we can reason about the influence of these preferences. Clever abstractions allow us to predict the outcome of complex agent interactions, however, as the scenarios we model increase in complexity, the abstractions we use to enable classical game-theoretic analysis lose fidelity. In empirical game-theoretic analysis, we construct game models using empirical sources of knowledge—such as high-fidelity simulation. However, utilizing empirical knowledge introduces a host of different computational and statistical problems. I investigate five main research problems that focus on efficient selection, estimation, and analysis of empirical game models. I introduce a flexible modeling approach, where we may construct multiple game-theoretic models from the same set of observations. I propose a principled methodology for comparing empirical game models and a family of algorithms that select a model from a set of candidates. I develop algorithms for normal-form games that efficiently identify formations—sets of strategies that are closed under a (correlated) best-response correspondence. This aids in problems, such as finding Nash equilibria, that are key to analysis but hard to solve. I investigate policies for sequentially determining profiles to simulate, when constrained by a budget for simulation. Efficient policies allow modelers to analyze complex scenarios by evaluating a subset of the profiles. The policies I introduce outperform the existing policies in experiments. I establish a principled methodology for evaluating strategies given an empirical game model. I employ this methodology in two case studies of market scenarios: first, a case study in supply chain management from the perspective of a strategy designer; then, a case study in Internet ad auctions from the perspective of a mechanism designer. As part of the latter analysis, I develop an ad-auctions scenario that captures several key strategic issues in this domain for the first time.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75848/1/prjordan_1.pd

Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system