Optimum polynomial retrieval functions based on the probability ranking principle
ACM Transactions on Information Systems (TOIS)
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the ninth international conference on Information and knowledge management
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking Function Optimization for Effective Web Search by Genetic Programming: An Empirical Study
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Closing the Gap: CPU and FPGA Trends in Sustainable Floating-Point BLAS Performance
FCCM '04 Proceedings of the 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Is high-performance reconfigurable computing the next supercomputing paradigm?
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
FRank: a ranking method with fidelity loss
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
FPMR: MapReduce framework on FPGA
Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays
LambdaRank acceleration for relevance ranking in web search engines (abstract only)
Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays
An FPGA-based accelerator for LambdaRank in Web search engines
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
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Search relevance is a key measurement for the usefulness of search engines. Shift of search relevance among search engines can easily change a search company's market cap by tens of billions of dollars. With the ever-increasing scale of the Web, machine learning technologies have become important tools to improve search relevance ranking. RankBoost is a promising algorithm in this area, but it is not widely used due to its long training time. To reduce the computation time for RankBoost, we designed a FPGA-based accelerator system and its upgraded version. The accelerator, plugged into a commodity PC, increased the training speed on MSN search engine data up to 1800x compared to the original software implementation on a server. The proposed accelerator has been successfully used by researchers in the search relevance ranking.