Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Optimizing query rewrites for keyword-based advertising
Proceedings of the 9th ACM conference on Electronic commerce
trNon-greedy active learning for text categorization using convex ansductive experimental design
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Simrank++: query rewriting through link analysis of the click graph
Proceedings of the VLDB Endowment
Active relevance feedback for difficult queries
Proceedings of the 17th ACM conference on Information and knowledge management
Efficient query expansion for advertisement search
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
Using landing pages for sponsored search ad selection
Proceedings of the 19th international conference on World wide web
An analysis of queries intended to search information for children
Proceedings of the third symposium on Information interaction in context
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
Sampling dilemma: towards effective data sampling for click prediction in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
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Sponsored search is a major revenue source for search companies. Web searchers can issue any queries, while advertisement keywords are limited. Query rewriting technique effectively matches user queries with relevant advertisement keywords, thus increases the amount of web advertisements available. The match relevance is critical for clicks. In this study, we aim to improve query rewriting relevance. For this purpose, we use an active learning algorithm called Transductive Experimental Design to select the most informative samples to train the query rewriting relevance model. Experiments show that this approach significantly improves model accuracy and rewriting relevance.