10 citations · 26 across the 8 of their papers we have counts for
4 papers · 1 filter
ILMART: Interpretable Ranking with Constrained LambdaMART
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most o…
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…
Design of a Parallel and Distributed Web Search Engine
Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri
This paper describes the architecture of MOSE (My Own Search Engine), a scalable parallel and distributed engine for searching the web. MOSE was specifically designed to efficientl…