Query evaluation: strategies and optimizations
Information Processing and Management: an International Journal
Self-indexing inverted files for fast text retrieval
ACM Transactions on Information Systems (TOIS)
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Efficient query evaluation using a two-level retrieval process
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Usefulness of hyperlink structure for query-biased topic distillation
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A framework for selective query expansion
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Pruned query evaluation using pre-computed impacts
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Incorporating term dependency in the dfr framework
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic feature selection in the markov random field model for information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Challenges in building large-scale information retrieval systems: invited talk
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Proceedings of the 2009 workshop on Web Search Click Data
Second ACM International Conference on Web Search and Web Data Mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Early exit optimizations for additive machine learned ranking systems
Proceedings of the third ACM international conference on Web search and data mining
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Estimating the Query Difficulty for Information Retrieval
Estimating the Query Difficulty for Information Retrieval
Quality-biased ranking of web documents
Proceedings of the fourth ACM international conference on Web search and data mining
Bagging gradient-boosted trees for high precision, low variance ranking models
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A cascade ranking model for efficient ranked retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Intent-aware search result diversification
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Upper-bound approximations for dynamic pruning
ACM Transactions on Information Systems (TOIS)
Efficient and effective spam filtering and re-ranking for large web datasets
Information Retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Learning to predict response times for online query scheduling
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Effect of dynamic pruning safety on learning to rank effectiveness
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Fast candidate generation for real-time tweet search with bloom filter chains
ACM Transactions on Information Systems (TOIS)
Load-sensitive selective pruning for distributed search
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Retrieval can be made more efficient by deploying dynamic pruning strategies such as WAND, which do not degrade effectiveness up to a given rank. It is possible to increase the efficiency of such techniques by pruning more 'aggressively'. However, this may reduce effectiveness. In this work, we propose a novel selective framework that determines the appropriate amount of pruning aggressiveness on a per-query basis, thereby increasing overall efficiency without significantly reducing overall effectiveness. We postulate two hypotheses about the queries that should be pruned more aggressively, which generate two approaches within our framework, based on query performance predictors and query efficiency predictors, respectively. We thoroughly experiment to ascertain the efficiency and effectiveness impacts of the proposed approaches, as part of a search engine deploying state-of-the-art learning to rank techniques. Our results on 50 million documents of the TREC ClueWeb09 collection show that by using query efficiency predictors to target inefficient queries, we observe that a 36% reduction in mean response time and a 50% reduction of the response times experienced by the slowest 10% of queries can be achieved while still ensuring effectiveness.