Add to ORKGScoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is currently deemed one of the best solutions to effectively rank query results to be returned by large scale Information Retrieval systems. This extended abstract shortly summarizes the work in [4] proposing V-QuickScorer (vQS), an algorithm which exploits SIMD vector extensions on modern CPUs to perform the traversal of the ensamble in parallel by evaluating multiple documents simultaneously. We summarize the results of a comprehensive evaluation of vQS against state-of-the-art scoring algorithms showing that vQS outperforms competitors with speed-ups up to a factor of 2.4x
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
The present invention concerns a novel method to efficiently score documents (texts, images, audios,...
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is...
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is...
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees cu...
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effec...
Machine-learnt models based on additive ensembles of binary regression trees are currently considere...
Machine-learnt models based on additive ensembles of binary regression trees are currently considere...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effec...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
The present invention concerns a novel method to efficiently score documents (texts, images, audios,...
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is...
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is...
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees cu...
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effec...
Machine-learnt models based on additive ensembles of binary regression trees are currently considere...
Machine-learnt models based on additive ensembles of binary regression trees are currently considere...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effec...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very ...
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed th...
The present invention concerns a novel method to efficiently score documents (texts, images, audios,...