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
Static index pruning for information retrieval systems
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
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
High accuracy retrieval with multiple nested ranker
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in 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
AdaRank: a boosting algorithm for information retrieval
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
On designing and deploying internet-scale services
LISA'07 Proceedings of the 21st conference on Large Installation System Administration Conference
Where to stop reading a ranked list?: threshold optimization using truncated score distributions
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Modeling the Score Distributions of Relevant and Non-relevant Documents
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Reducing the risk of query expansion via robust constrained optimization
Proceedings of the 18th ACM conference on Information and knowledge management
A case study of distributed information retrieval architectures to index one terabyte of text
Information Processing and Management: an International Journal
Learning concept importance using a weighted dependence model
Proceedings of the third ACM international conference on Web search and data mining
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
Ranking under temporal constraints
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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
Empirical comparisons of various discriminative language models for speech recognition
ROCLING '11 Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing
To index or not to index: time-space trade-offs in search engines with positional ranking functions
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
A math-aware search engine for math question answering system
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient and effective retrieval using selective pruning
Proceedings of the sixth ACM international conference on Web search and data mining
Optimizing top-k document retrieval strategies for block-max indexes
Proceedings of the sixth ACM international conference on Web search and data mining
ExpertRank: A topic-aware expert finding algorithm for online knowledge communities
Decision Support Systems
Training efficient tree-based models for document ranking
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
A candidate filtering mechanism for fast top-k query processing on modern cpus
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Faster and smaller inverted indices with treaps
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)
Permutation indexing: fast approximate retrieval from large corpora
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Indexing Word Sequences for Ranked Retrieval
ACM Transactions on Information Systems (TOIS)
Document vector representations for feature extraction in multi-stage document ranking
Information Retrieval
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There is a fundamental tradeoff between effectiveness and efficiency when designing retrieval models for large-scale document collections. Effectiveness tends to derive from sophisticated ranking functions, such as those constructed using learning to rank, while efficiency gains tend to arise from improvements in query evaluation and caching strategies. Given their inherently disjoint nature, it is difficult to jointly optimize effectiveness and efficiency in end-to-end systems. To address this problem, we formulate and develop a novel cascade ranking model, which unlike previous approaches, can simultaneously improve both top k ranked effectiveness and retrieval efficiency. The model constructs a cascade of increasingly complex ranking functions that progressively prunes and refines the set of candidate documents to minimize retrieval latency and maximize result set quality. We present a novel boosting algorithm for learning such cascades to directly optimize the tradeoff between effectiveness and efficiency. Experimental results show that our cascades are faster and return higher quality results than comparable ranking models.