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- item-3 hasContent s-muthukrishnan.
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- item-4 hasContent yishay-mansour.
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- 25 type InProceedings.
- 25 label "General Auction Mechanism for Search Advertising".
- 25 sameAs 25.
- 25 abstract "In sponsored search, a number of advertising slots is available on a search results page, and have to be allocated among a set of advertisers competing to display an ad on the page. This gives rise to a bipartite matching market that is typically cleared by the way of an automated auction. Several auction mechanisms have been proposed, with variants of the Generalized Second Price (GSP) being widely used in practice. There is a rich body of work on bipartite matching markets that builds upon the stable marriage model of Gale and Shapley and the assignment model of Shapley and Shubik. This line of research offers deep insights into the structure of stable outcomes in such markets and their incentive properties. In this paper, we model advertising auctions in terms of an assignment model with linear utilities, extended with bidder and item specific maximum and minimum prices. Auction mechanisms like the commonly used GSP or the well-known Vickrey-Clarke-Groves (VCG) can be interpreted as simply computing a bidder-optimal stable matching in this model, for a suitably defined set of bidder preferences, but our model includes much richer bidders and preferences. We prove that in our model the existence of a stable matching is guaranteed, and under a non-degeneracy assumption a bidder-optimal stable matching exists as well. We give an algorithm to find such matching in polynomial time, and use it to design truthful mechanism that generalizes GSP, is truthful for profitmaximizing bidders, correctly implements features like bidder-specific minimum prices and position-specific bids, and works for rich mixtures of bidders and preferences. Our main technical contributions are the existence of bidder-optimal matchings and strategyproofness of the resulting mechanism, and are proved by induction on the progress of the matching algorithm.".
- 25 hasAuthorList authorList.
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- 25 keyword "Internet Monetization".
- 25 title "General Auction Mechanism for Search Advertising".
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- item-1 hasContent gagan-aggarwal.
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- item-2 hasContent s-muthukrishnan.
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- item-3 hasContent david-pal.
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- item-4 hasContent martin-pal.
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- 26 type InProceedings.
- 26 label "Adaptive Bidding for Display Advertising".
- 26 sameAs 26.
- 26 abstract "Motivated by the emergence of auction-based marketplaces for display ads such as the Right Media Exchange, we study the design of a bidding agent that implements a display advertising campaign by bidding in such a marketplace. The bidding agent must acquire a given number of impressions with a given target spend, when the highest external bid in the marketplace is drawn from an unknown distribution P . The quantity and spend constraints arise from the fact that display ads are usually sold on a CPM basis. We consider both the full information setting, where the winning price in each auction is announced publicly, and the partially observable setting where only the winner obtains information about the distribution; these differ in the penalty incurred by the agent while attempting to learn the distribution. We provide algorithms for both settings, and prove performance guarantees using bounds on uniform closeness from statistics, and techniques from online learning. We experimentally evaluate these algorithms: both algorithms perform very well with respect to both target quantity and spend; further, our algorithm for the partially observable case performs nearly as well as that for the fully observable setting despite the higher penalty incurred during learning.".
- 26 hasAuthorList authorList.
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- 26 keyword "Internet Monetization".
- 26 title "Adaptive Bidding for Display Advertising".
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- item-1 hasContent arpita-ghosh.
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- item-4 hasContent martin-zinkevich.
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- 27 type InProceedings.
- 27 label "How Much Can Behavioral Targeting Help Online Advertising?".
- 27 sameAs 27.
- 27 abstract "Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia how much BT can truly help online advertising in search engines. In this paper we provide an empirical study on the click-through log of advertisements collected from a commercial search engine. From the experiment results over a period of seven days, we draw three important conclusions: (1) Users who clicked the same ad will truly have similar behaviors on the Web; (2) Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search; (3) Using short term user behaviors to represent users is more effective than using long term user behaviors for BT. We conducted statistical t-test which verified that all conclusions drawn in the paper are statistically significant. To the best of our knowledge, this work is the first empirical study for BT on the click-through log of real world ads.".
- 27 hasAuthorList authorList.
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- 27 keyword "Internet Monetization".
- 27 title "How Much Can Behavioral Targeting Help Online Advertising?".
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- item-1 hasContent jun-yan.
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- item-2 hasContent ning-liu.
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- item-3 hasContent gang-wang.
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