A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
INFORMS Journal on Computing
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
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
Search advertising using web relevance feedback
Proceedings of the 17th ACM conference on Information and knowledge management
Optimizing search engine revenue in sponsored search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Multi-objective optimization for sponsored search
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
A game- heoretic machine learning approach for revenue maximization in sponsored search
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Displaying sponsored ads alongside the search results is a key monetization strategy for search engine companies. Since users are more likely to click ads that are relevant to their query, it is crucial for search engine to deliver the right ads for the query and the order in which they are displayed. There are several works investigating on how to learn a ranking function to maximize the number of ad clicks. However, this ranking optimization problem is different from algorithmic search results ranking in that the ranking scheme must take received revenue into account in order to make more profit for the search engines. In this paper, we address a new optimization problem and aim to answer the question: how to construct a ranking model that can deliver high quality ads to the user as well as maximize search engine revenue? We introduce a novel tradeoff method from machine learning perspective, and through this method we have the privilege of choosing a tradeoff parameter to achieve highest relevance ranking or highest revenue ranking or the tradeoff between them. The algorithms are built upon the click-through log data with real ad clicks and impressions. The extensively experimental results verify that the proposed algorithm has the property that the search engine could choose a proper parameter to achieve high revenue(income) without losing to much relevance.