A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
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
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Estimating rates of rare events at multiple resolutions
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving search engines by query clustering
Journal of the American Society for Information Science and Technology
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
Unsupervised query segmentation using generative language models and wikipedia
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
Search advertising using web relevance feedback
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
Online expansion of rare queries for sponsored search
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
Extracting structured information from user queries with semi-supervised conditional random fields
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
Learning user clicks in web search
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Estimating Ad Clickthrough Rate through Query Intent Analysis
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Improving ad relevance in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
Temporal click model for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Overlapping experiment infrastructure: more, better, faster experimentation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
User browsing models: relevance versus examination
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic first pass retrieval for search advertising: from theory to practice
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Post-click conversion modeling and analysis for non-guaranteed delivery display advertising
Proceedings of the fifth ACM international conference on Web search and data mining
Relational click prediction for sponsored search
Proceedings of the fifth ACM international conference on Web search and data mining
Position-normalized click prediction in search advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-objective optimization for sponsored search
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
Psychological advertising: exploring user psychology for click prediction in sponsored search
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving contextual advertising by adopting collaborative filtering
ACM Transactions on the Web (TWEB)
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The critical task of predicting clicks on search advertisements is typically addressed by learning from historical click data. When enough history is observed for a given query-ad pair, future clicks can be accurately modeled. However, based on the empirical distribution of queries, sufficient historical information is unavailable for many query-ad pairs. The sparsity of data for new and rare queries makes it difficult to accurately estimate clicks for a significant portion of typical search engine traffic. In this paper we provide analysis to motivate modeling approaches that can reduce the sparsity of the large space of user search queries. We then propose methods to improve click and relevance models for sponsored search by mining click behavior for partial user queries. We aggregate click history for individual query words, as well as for phrases extracted with a CRF model. The new models show significant improvement in clicks and revenue compared to state-of-the-art baselines trained on several months of query logs. Results are reported on live traffic of a commercial search engine, in addition to results from offline evaluation.