A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Approximating min-sum k-clustering in metric spaces
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Combinatorial auctions with decreasing marginal utilities
Proceedings of the 3rd ACM conference on Electronic Commerce
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Introduction to Linear Optimization
Introduction to Linear Optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Truth revelation in approximately efficient combinatorial auctions
Journal of the ACM (JACM)
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Expander flows, geometric embeddings and graph partitioning
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Approximating Min Sum Set Cover
Algorithmica
Combinatorial Auctions
Tight approximation algorithms for maximum general assignment problems
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
On maximizing welfare when utility functions are subadditive
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Truthful randomized mechanisms for combinatorial auctions
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Allocating online advertisement space with unreliable estimates
Proceedings of the 8th ACM conference on Electronic commerce
AdWords and generalized online matching
Journal of the ACM (JACM)
Maximizing Non-Monotone Submodular Functions
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Online primal-dual algorithms for maximizing ad-auctions revenue
ESA'07 Proceedings of the 15th annual European conference on Algorithms
An online mechanism for ad slot reservations with cancellations
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Selling ad campaigns: online algorithms with cancellations
Proceedings of the 10th ACM conference on Electronic commerce
Online allocation of display advertisements subject to advanced sales contracts
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
APPROX '09 / RANDOM '09 Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Bidding for Representative Allocations for Display Advertising
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Online Ad Assignment with Free Disposal
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Balanced allocation with succinct representation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time bidding algorithms for performance-based display ad allocation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Whole-page optimization and submodular welfare maximization with online bidders
Proceedings of the fourteenth ACM conference on Electronic commerce
Forecasting user visits for online display advertising
Information Retrieval
New online algorithms for story scheduling in web advertising
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
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Most current banner advertising is sold through negotiation thereby incurring large transaction costs and possibly suboptimal allocations. We propose a new automated system for selling banner advertising. In this system, each advertiser specifies a collection of host webpages which are relevant to his product, a desired total quantity of impressions on these pages, and a maximum per-impression price. The system selects a subset of advertisers as 'winners' and maps each winner to a set of impressions on pages within his desired collection. The distinguishing feature of our system as opposed to current combinatorial allocation mechanisms is that, mimicking the current negotiation system, we guarantee that winners receive at least as many advertising opportunities as they requested or else receive ample compensation in the form of a monetary payment by the host. Such guarantees are essential in markets like banner advertising where a major goal of the advertising campaign is developing brand recognition. As we show, the problem of selecting a feasible subset of advertisers with maximum total value is inapproximable. We thus present two greedy heuristics and discuss theoretical techniques to measure their performances. Our first algorithm iteratively selects advertisers and corresponding sets of impressions which contribute maximum marginal per-impression profit to the current solution. We prove a bi-criteria approximation for this algorithm, showing that it generates approximately as much value as the optimum algorithm on a slightly harder problem. However, this algorithm might perform poorly on instances in which the value of the optimum solution is quite large, a clearly undesirable failure mode. Hence, we present an adaptive greedy algorithm which again iteratively selects advertisers with maximum marginal per-impression profit, but additionally reassigns impressions at each iteration. For this algorithm, we prove a structural approximation result, a newly defined framework for evaluating heuristics [10]. We thereby prove that this algorithm has a better performance guarantee than the simple greedy algorithm.