Query evaluation: strategies and optimizations
Information Processing and Management: an International Journal
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Building a distributed full-text index for the web
ACM Transactions on Information Systems (TOIS)
Modern Information Retrieval
Database System Implementation
Database System Implementation
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Efficient single-pass index construction for text databases
Journal of the American Society for Information Science and Technology
Efficient query evaluation using a two-level retrieval process
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Marketing Science
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Optimized query execution in large search engines with global page ordering
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
High performance index build algorithms for intranet search engines
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Framework for timely and accurate ads on mobile devices
Proceedings of the 18th ACM conference on Information and knowledge management
Contextual advertising using keyword extraction through collocation
Proceedings of the 7th International Conference on Frontiers of Information Technology
Bid landscape forecasting in online ad exchange marketplace
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic generation of listing ads by reusing promotional texts
Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business
Ad impression forecasting for sponsored search
Proceedings of the 22nd international conference on World Wide Web
Forecasting user visits for online display advertising
Information Retrieval
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Contextual advertising (also called content match) refers to the placement of small textual ads within the content of a generic web page. It has become a significant source of revenue for publishers ranging from individual bloggers to major newspapers. At the same time it is an important way for advertisers to reach their intended audience. This reach depends on the total number of exposures of the ad (impressions) and its click-through-rate (CTR) that can be viewed as the probability of an end-user clicking on the ad when shown. These two orthogonal, critical factors are both difficult to estimate and even individually can still be very informative and useful in planning and budgeting advertising campaigns. In this paper, we address the problem of forecasting the number of impressions for new or changed ads in the system. Producing such forecasts, even within large margins of error, is quite challenging: 1) ad selection in contextual advertising is a complicated process based on tens or even hundreds of page and ad features; 2) the publishers' content and traffic vary over time; and 3) the scale of the problem is daunting: over a course of a week it involves billions of impressions, hundreds of millions of distinct pages, hundreds of millions of ads, and varying bids of other competing advertisers. We tackle these complexities by simulating the presence of a given ad with its associated bid over weeks of historical data. We obtain an impression estimate by counting how many times the ad would have been displayed if it were in the system over that period of time. We estimate this count by an efficient two-level search algorithm over the distinct pages in the data set. Experimental results show that our approach can accurately forecast the expected number of impressions of contextual ads in real time. We also show how this method can be used in tools for bid selection and ad evaluation.