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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
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Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Multi-armed bandit problems with dependent arms
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
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A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
How well does result relevance predict session satisfaction?
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Predictive user click models based on click-through history
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Online learning from click data for sponsored search
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Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
PLANET: massively parallel learning of tree ensembles with MapReduce
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Improving ad relevance in sponsored search
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Exploring searcher interactions for distinguishing types of commercial intent
Proceedings of the 19th international conference on World wide web
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Incorporating post-click behaviors into a click model
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User browsing models: relevance versus examination
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Combined regression and ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Minimally-supervised extraction of entities from text advertisements
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Inferring search behaviors using partially observable markov model with duration (POMD)
Proceedings of the fourth ACM international conference on Web search and data mining
Action prediction and identification from mining temporal user behaviors
Proceedings of the fourth ACM international conference on Web search and data mining
Is pay-per-click efficient?: an empirical analysis of click values
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The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting adversarial advertisements in the wild
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Mean field equilibria of dynamic auctions with learning
ACM SIGecom Exchanges
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
An ontology-based approach to Chinese semantic advertising
Information Sciences: an International Journal
Multi-objective optimization for sponsored search
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ACM Transactions on the Web (TWEB)
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This paper explores an important and relatively unstudied quality measure of a sponsored search advertisement: bounce rate. The bounce rate of an ad can be informally defined as the fraction of users who click on the ad but almost immediately move on to other tasks. A high bounce rate can lead to poor advertiser return on investment, and suggests search engine users may be having a poor experience following the click. In this paper, we first provide quantitative analysis showing that bounce rate is an effective measure of user satisfaction. We then address the question, can we predict bounce rate by analyzing the features of the advertisement? An affirmative answer would allow advertisers and search engines to predict the effectiveness and quality of advertisements before they are shown. We propose solutions to this problem involving large-scale learning methods that leverage features drawn from ad creatives in addition to their keywords and landing pages.