Implementations of partial document ranking using inverted files
Information Processing and Management: an International Journal
Document filtering for fast ranking
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Fast evaluation of structured queries for information retrieval
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Query evaluation: strategies and optimizations
Information Processing and Management: an International Journal
Machine Learning
Self-indexing inverted files for fast text retrieval
ACM Transactions on Information Systems (TOIS)
Optimization of inverted vector searches
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Combining fuzzy information from multiple systems
Journal of Computer and System Sciences
Vector-space ranking with effective early termination
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Combining fuzzy information: an overview
ACM SIGMOD Record
Introduction to Algorithms
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Predictive caching and prefetching of query results in search engines
WWW '03 Proceedings of the 12th international conference on World Wide Web
Robust Real-Time Face Detection
International Journal of Computer Vision
Optimization strategies for complex queries
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th 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
Optimisation methods for ranking functions with multiple parameters
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Hits on the web: how does it compare?
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Top-k aggregation using intersections of ranked inputs
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Quantifying performance and quality gains in distributed web search engines
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Ranking under temporal constraints
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A cascade ranking model for efficient ranked retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Posting list intersection on multicore architectures
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Optimized top-k processing with global page scores on block-max indexes
Proceedings of the fifth ACM international conference on Web search and data mining
Fast top-k retrieval for model based recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Algorithms and Applications
LePrEF: Learn to precompute evidence fusion for efficient query evaluation
Journal of the American Society for Information Science and Technology
Reactive index replication for distributed search engines
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Cache-Based Query Processing for Search Engines
ACM Transactions on the Web (TWEB)
Document replication strategies for geographically distributed web search engines
Information Processing and Management: an International Journal
Efficient and effective retrieval using selective pruning
Proceedings of the sixth ACM international conference on Web search and data mining
Training efficient tree-based models for document ranking
ECIR'13 Proceedings of the 35th European conference on Advances 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
About learning models with multiple query-dependent features
ACM Transactions on Information Systems (TOIS)
Fast candidate generation for real-time tweet search with bloom filter chains
ACM Transactions on Information Systems (TOIS)
The whens and hows of learning to rank for web search
Information Retrieval
Document vector representations for feature extraction in multi-stage document ranking
Information Retrieval
Evaluation as a service for information retrieval
ACM SIGIR Forum
Journal of Signal Processing Systems
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Some commercial web search engines rely on sophisticated machine learning systems for ranking web documents. Due to very large collection sizes and tight constraints on query response times, online efficiency of these learning systems forms a bottleneck. An important problem in such systems is to speedup the ranking process without sacrificing much from the quality of results. In this paper, we propose optimization strategies that allow short-circuiting score computations in additive learning systems. The strategies are evaluated over a state-of-the-art machine learning system and a large, real-life query log, obtained from Yahoo!. By the proposed strategies, we are able to speedup the score computations by more than four times with almost no loss in result quality.