Boosting support vector machines for text classification through parameter-free threshold relaxation
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing relevance and revenue in ad search: a query substitution approach
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
To swing or not to swing: learning when (not) to advertise
Proceedings of the 17th ACM conference on Information and knowledge management
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Click chain model in web search
Proceedings of the 18th international conference on World wide web
Spatio-temporal models for estimating click-through rate
Proceedings of the 18th international conference on World wide web
Predicting click through rate for job listings
Proceedings of the 18th international conference on World wide web
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Click-through prediction for news queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Where to stop reading a ranked list?: threshold optimization using truncated score distributions
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engine revenue in sponsored search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Heterogeneous cross domain ranking in latent space
Proceedings of the 18th ACM conference on Information and knowledge management
A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
Actively predicting diverse search intent from user browsing behaviors
Proceedings of the 19th international conference on World wide web
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Sponsored advertisement(ad) has already become the major source of revenue for most popular search engines. One fundamental challenge facing all search engines is how to achieve a balance between the number of displayed ads and the potential annoyance to the users. Displaying more ads would improve the chance for the user clicking an ad. However, when the ads are not really relevant to the users' interests, displaying more may annoy them and even "train" them to ignore ads. In this paper, we study an interesting problem that how many ads should be displayed for a given query. We use statistics on real ads click-through data to show the existence of the problem and the possibility to predict the ideal number. There are two main observations: 1) when the click entropy of a query exceeds a threshold, the CTR of that query will be very near zero; 2) the threshold of click entropy can be automatically determined when the number of removed ads is given. Further, we propose a learning approach to rank the ads and to predict the number of displayed ads for a given query. The experimental results on a commercial search engine dataset validate the effectiveness of the proposed approach.