Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of long queries in a large scale search log
Proceedings of the 2009 workshop on Web Search Click Data
Reducing long queries using query quality predictors
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Exploring reductions for long web queries
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Rewriting null e-commerce queries to recommend products
Proceedings of the 21st international conference companion on World Wide Web
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Verbose web queries are often descriptive in nature where a term based search engine is unable to distinguish between the essential and noisy words, which can result in a drift from the user intent. We present a randomized query reduction technique that builds on an earlier learning to rank based approach. The proposed technique randomly picks only a small set of samples, instead of the exponentially many sub-queries, thus being fast enough to be useful for web search engines, while still covering wide sub-query space.