SIGIR '02 Proceedings of the 25th 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
Unsupervised query segmentation using generative language models and wikipedia
Proceedings of the 17th international conference on World Wide Web
A unified and discriminative model for query refinement
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Improved query difficulty prediction for the web
Proceedings of the 17th ACM conference on Information and knowledge management
Reducing long queries using query quality predictors
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A Query Substitution-Search Result Refinement Approach for Long Query Web Searches
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Exploring reductions for long web queries
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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Commercial search engines do not return optimal search results when the query is a long or multi-topic one [1]. Long queries are used extensively. While the creator of the long query would most likely use natural language to describe the query, it usually contains extra information. This information dilutes the results of a web search, and hence decreases the performance as well as the quality of the results returned. Kumaran et al. [14] showed that shorter queries extracted from longer user generated queries are more effective for ad-hoc retrieval. Hence, reducing the query length by removing extra terms improves the quality of the search results. There are numerous approaches used to address this shortfall. Our approach evaluates various versions of the query, trying to find the optimal one. This variation is achieved by reducing the query length using a random keyword combination. We use existing models and plug in information with the help of randomization to improve the overall performance while keeping any overhead calculations in check.