The Continuum-Armed Bandit Problem
SIAM Journal on Control and Optimization
Approximate Algorithms for the 0/1 Knapsack Problem
Journal of the ACM (JACM)
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
PAC Bounds for Multi-armed Bandit and Markov Decision Processes
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Online decision problems with large strategy sets
Online decision problems with large strategy sets
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
Dynamics of bid optimization in online advertisement auctions
Proceedings of the 16th international conference on World Wide Web
Budget optimization in search-based advertising auctions
Proceedings of the 8th ACM conference on Electronic commerce
An empirical analysis of sponsored search performance in search engine advertising
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Online budgeted matching in random input models with applications to Adwords
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
A Knapsack Secretary Problem with Applications
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
The ratio index for budgeted learning, with applications
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Comparing performance metrics in organic search with sponsored search advertising
Proceedings of the 2nd International Workshop on Data Mining and Audience Intelligence for Advertising
Bid optimization for broad match ad auctions
Proceedings of the 18th international conference on World wide web
Selling ad campaigns: online algorithms with cancellations
Proceedings of the 10th ACM conference on Electronic commerce
Expressive banner ad auctions and model-based online optimization for clearing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Stochastic models for budget optimization in search-based advertising
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Empirical price modeling for sponsored search
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Designing a Successful Adaptive Agent for TAC Ad Auction
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Keyword auction protocol for dynamically adjusting the number of advertisements
Web Intelligence and Agent Systems
Multi-keyword sponsored search
Proceedings of the 12th ACM conference on Electronic commerce
IEEE/ACM Transactions on Networking (TON)
Optimizing budget constrained spend in search advertising
Proceedings of the sixth ACM international conference on Web search and data mining
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Increases in online search activities have spurred the growth of search-based advertising services offered by search engines. These services enable companies to promote their products to consumers based on their search queries. In most search-based advertising services, a company sets a daily budget, selects a set of keywords, determines a bid price for each keyword, and designates an ad associated with each selected keyword. When a consumer searches for one of the selected keywords, search engines then display the ads associated with the highest bids for that keyword on the search result page. A company whose ad is displayed pays the search engine only when the consumer clicks on the ad. If the company's spending has exceeded its daily budget, however, its ads will not be displayed. With millions of available keywords and a highly uncertain clickthru rate associated with the ad for each keyword, identifying the most profitable set of keywords given the daily budget constraint becomes challenging for companies wishing to promote their goods and services via search-based advertising. Motivated by these challenges, we formulate a model of keyword selection in search-based advertising services. We develop an algorithm that adaptively identifies the set of keywords to bid on based on historical performance. The algorithm prioritizes keywords based on a prefix ordering-- sorting of keywords in a descending order of profit-tocost ratio (or "bang-per-buck"). We show that the average expected profit generated by the algorithm converges to near-optimal profits. Furthermore, the convergence rate is independent of the number of keywords and scales gracefully with the problem's parameters. Extensive numerical simulations show that our algorithm outperforms existing methods, increasing profits by about 7%.