Inferring probability of relevance using the method of logistic regression
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Static index pruning for information retrieval systems
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Optimization strategies for complex queries
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Term proximity scoring for ad-hoc retrieval on very large text collections
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Linear feature-based models for information retrieval
Information Retrieval
The impact of caching on search engines
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Pruning policies for two-tiered inverted index with correctness guarantee
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An exploration of proximity measures in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Investigation of partial query proximity in web search
Proceedings of the 17th international conference on World Wide Web
An improved markov random field model for supporting verbose queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Two-stage query segmentation for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Learning concept importance using a weighted dependence model
Proceedings of the third ACM international conference on Web search and data mining
Ranking under temporal constraints
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning to rank for freshness and relevance
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
Efficient manifold ranking for image retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Multi-objective optimization in learning to rank
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rule-based active sampling for learning to rank
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
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
Efficient and effective retrieval using selective pruning
Proceedings of the sixth ACM international conference on Web search and data mining
Modeling reformulation using query distributions
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
A self-adapting latency/power tradeoff model for replicated search engines
Proceedings of the 7th ACM international conference on Web search and data mining
Journal of Signal Processing Systems
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It has been shown that learning to rank approaches are capable of learning highly effective ranking functions. However, these approaches have mostly ignored the important issue of efficiency. Given that both efficiency and effectiveness are important for real search engines, models that are optimized for effectiveness may not meet the strict efficiency requirements necessary to deploy in a production environment. In this work, we present a unified framework for jointly optimizing effectiveness and efficiency. We propose new metrics that capture the tradeoff between these two competing forces and devise a strategy for automatically learning models that directly optimize the tradeoff metrics. Experiments indicate that models learned in this way provide a good balance between retrieval effectiveness and efficiency. With specific loss functions, learned models converge to familiar existing ones, which demonstrates the generality of our framework. Finally, we show that our approach naturally leads to a reduction in the variance of query execution times, which is important for query load balancing and user satisfaction.