153 citations · 162 across the 4 of their papers we have counts for
3 papers · 1 filter
Learning Early Exit Strategies for Additive Ranking Ensembles
Francesco Busolin, Claudio Lucchese, Franco Maria Nardini +3
Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the…
Query-level Early Exit for Additive Learning-to-Rank Ensembles
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2
Search engine ranking pipelines are commonly based on large ensembles of machine-learned decision trees. The tight constraints on query response time recently motivated researchers…
Computing Entity Semantic Similarity by Features Ranking
Livia Ruback, Claudio Lucchese, Alexander Arturo Mera Caraballo +3
This article presents a novel approach to estimate semantic entity similarity using entity features available as Linked Data. The key idea is to exploit ranked lists of features, e…