Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Predicting clicks: estimating the click-through rate for new ads
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
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
Incorporating user utility into sponsored-search auctions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Predicting bounce rates in sponsored search advertisements
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing search engine revenue in sponsored search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A relevance model based filter for improving ad quality
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Data-driven text features for sponsored search click prediction
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Revenue optimization with relevance constraint in sponsored search
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
Improving ad relevance in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
Proceedings of the 19th international conference on World wide web
Explore click models for search ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
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Sponsored search has been recognized as one of the major internet monetization solutions for commercial search engines. There are generally three types of participants in this online advertising problem, who are search users, advertisers and publishers. Though previous studies have proposed to optimize for different participants independently, it is underexplored how to optimize for all participants in a unified framework and in a systematic way. In this paper, we propose to model the ad ranking problem in sponsored search as a Multi-Objective Optimization (MOO) problem for all participants. We show that many previous studies are special cases of the MOO framework. Taking advantage from the Pareto solution set of MOO, we can easily find more optimized solutions with significant improvement in one objective and minor sacrifice in others. This enables a more flexible way for us to tradeoff among different participants, i.e. objective functions, in sponsored search. Besides the empirical studies for comparing MOO with related previous sponsored search studies, we provide the insightful applications of MOO framework, which is a prediction model to help users determine the tradeoff parameters among different objective functions. Experimental results show the outstanding performance of the proposed prediction model for parameter selection in ad ranking optimization.