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Article: Whole-Page Optimization and Submodular Welfare Maximization with Online Bidders

TitleWhole-Page Optimization and Submodular Welfare Maximization with Online Bidders
Authors
Issue Date2016
PublisherACM Special Interest Group. The Journal's web site is located at http://teac.acm.org/
Citation
ACM Transactions on Economics and Computation, 2016, v. 4 n. 3, article no. 14 How to Cite?
AbstractIn the context of online ad serving, display ads may appear on different types of webpages, where each page includes several ad slots and therefore multiple ads can be shown on each page. The set of ads that can be assigned to ad slots of the same page needs to satisfy various prespecified constraints including exclusion constraints, diversity constraints, and the like. Upon arrival of a user, the ad serving system needs to allocate a set of ads to the current webpage respecting these per-page allocation constraints. Previous slot-based settings ignore the important concept of a page and may lead to highly suboptimal results in general. In this article, motivated by these applications in display advertising and inspired by the submodular welfare maximization problem with online bidders, we study a general class of page-based ad allocation problems, present the first (tight) constant-factor approximation algorithms for these problems, and confirm the performance of our algorithms experimentally on real-world datasets. A key technical ingredient of our results is a novel primal-dual analysis for handling free disposal, which updates dual variables using a “level function” instead of a single level and unifies with previous analyses of related problems. This new analysis method allows us to handle arbitrarily complicated allocation constraints for each page. Our main result is an algorithm that achieves a 1 &minus frac 1 e &minus o(1)-competitive ratio. Moreover, our experiments on real-world datasets show significant improvements of our page-based algorithms compared to the slot-based algorithms. Finally, we observe that our problem is closely related to the submodular welfare maximization (SWM) problem. In particular, we introduce a variant of the SWM problem with online bidders and show how to solve this problem using our algorithm for whole-page optimization.
Persistent Identifierhttp://hdl.handle.net/10722/232834
ISSN

 

DC FieldValueLanguage
dc.contributor.authorDevanur, NR-
dc.contributor.authorHuang, Z-
dc.contributor.authorKorula, N-
dc.contributor.authorMirrokni, V-
dc.contributor.authorYan, Q-
dc.date.accessioned2016-09-20T05:32:47Z-
dc.date.available2016-09-20T05:32:47Z-
dc.date.issued2016-
dc.identifier.citationACM Transactions on Economics and Computation, 2016, v. 4 n. 3, article no. 14-
dc.identifier.issn2167-8375-
dc.identifier.urihttp://hdl.handle.net/10722/232834-
dc.description.abstractIn the context of online ad serving, display ads may appear on different types of webpages, where each page includes several ad slots and therefore multiple ads can be shown on each page. The set of ads that can be assigned to ad slots of the same page needs to satisfy various prespecified constraints including exclusion constraints, diversity constraints, and the like. Upon arrival of a user, the ad serving system needs to allocate a set of ads to the current webpage respecting these per-page allocation constraints. Previous slot-based settings ignore the important concept of a page and may lead to highly suboptimal results in general. In this article, motivated by these applications in display advertising and inspired by the submodular welfare maximization problem with online bidders, we study a general class of page-based ad allocation problems, present the first (tight) constant-factor approximation algorithms for these problems, and confirm the performance of our algorithms experimentally on real-world datasets. A key technical ingredient of our results is a novel primal-dual analysis for handling free disposal, which updates dual variables using a “level function” instead of a single level and unifies with previous analyses of related problems. This new analysis method allows us to handle arbitrarily complicated allocation constraints for each page. Our main result is an algorithm that achieves a 1 &minus frac 1 e &minus o(1)-competitive ratio. Moreover, our experiments on real-world datasets show significant improvements of our page-based algorithms compared to the slot-based algorithms. Finally, we observe that our problem is closely related to the submodular welfare maximization (SWM) problem. In particular, we introduce a variant of the SWM problem with online bidders and show how to solve this problem using our algorithm for whole-page optimization.-
dc.languageeng-
dc.publisherACM Special Interest Group. The Journal's web site is located at http://teac.acm.org/-
dc.relation.ispartofACM Transactions on Economics and Computation-
dc.rightsACM Transactions on Economics and Computation. Copyright © ACM Special Interest Group.-
dc.rights©ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, ACM Transactions on Economics and Computation, 2016, v. 4 n. 3, article no. 14. http://doi.acm.org/10.1145//2892563-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleWhole-Page Optimization and Submodular Welfare Maximization with Online Bidders-
dc.typeArticle-
dc.identifier.emailHuang, Z: zhiyi@cs.hku.hk-
dc.identifier.authorityHuang, Z=rp01804-
dc.description.naturepostprint-
dc.identifier.doi10.1145/2892563-
dc.identifier.hkuros263128-
dc.identifier.volume4-
dc.identifier.issue3-
dc.identifier.spagearticle no. 14-
dc.identifier.epagearticle no. 14-
dc.publisher.placeUnited States-

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