RankEval: Evaluation and investigation of ranking models

Autor: Claudio Lucchese, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Salvatore Trani

Publikováno v: SoftwareX, Vol 12, Iss , Pp 100614- (2020)

RankEval is a Python open-source tool for the analysis and evaluation of ranking models based on ensembles of decision trees. Learning-to-Rank (LtR) approaches that generate tree-ensembles are considered the most effective solution for difficult rank

Externí odkaz: https://doaj.org/article/5a6ce575423a45dc80eb79de607e1d04

On Learning Prediction Models for Tourists Paths

Autor: Ranieri Baraglia, Fabrizio Silvestri, Cristina Ioana Muntean, Franco Maria Nardini

Publikováno v: ACM transactions on intelligent systems and technology
7 (2015): 8–35. doi:10.1145/2766459
info:cnr-pdr/source/autori:Muntean C.I.; Nardini F.M.; Silvestri F.; Baraglia R./titolo:On learning prediction models for tourists paths/doi:10.1145%2F2766459/rivista:ACM transactions on intelligent systems and technology (Print)/anno:2015/pagina_da:8/pagina_a:35/intervallo_pagine:8–35/volume:7

In this article, we tackle the problem of predicting the “next” geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist’s current trail) by means of supervised learning techniques, namely G

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4888e150ff6c37950f10f3596b063bab
https://zenodo.org/record/8128600

Efficient traversal of decision tree ensembles with FPGAs

Autor: Veronica Gil-Costa, Fernando Loor, Salvatore Trani, Franco Maria Nardini, Romina Soledad Molina, Raffaele Perego

Publikováno v: Journal of parallel and distributed computing
155 (2021): 38–49. doi:10.1016/j.jpdc.2021.04.008
info:cnr-pdr/source/autori:Molina R.; Loor F.; Gil-Costa V.; Nardini F.M.; Perego R.; Trani S./titolo:Efficient traversal of decision tree ensembles with FPGAs/doi:10.1016%2Fj.jpdc.2021.04.008/rivista:Journal of parallel and distributed computing (Print)/anno:2021/pagina_da:38/pagina_a:49/intervallo_pagine:38–49/volume:155

System-on-Chip (SoC) based Field Programmable Gate Arrays (FPGAs) provide a hardware acceleration technology that can be rapidly deployed and tuned, thus providing a flexible solution adaptable to specific design requirements and to changing demands.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4474a5ce69ea7a2daf920550cb8c747a
https://doi.org/10.1016/j.jpdc.2021.04.008

ILMART: Interpretable Ranking with Constrained LambdaMART

Autor: Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri

Publikováno v: SIGIR '22-45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2255–2259, Madrid, Spain, 11-15/07/2022

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 of the previous research efforts focus on creating post-hoc explanation

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7090e7c764ef7b847f6291026a1278e6
https://doi.org/10.1145/3477495.3531840

RICH: Research and Innovation for Cultural Heritage

Autor: Vincenzo Barone, Daniele Licari, Franco Maria Nardini

Publikováno v: Conservation Science in Cultural Heritage, Vol 12, Iss 1, Pp 109-133 (2012)

This paper describes RICH: a new architecture conceived and developed at the Scuola Normale Superiore, for collecting, promoting and sharing cultural heritage data. Starting with the observation that cultural heritage is a cross-cutting field of rese

Externí odkaz: https://doaj.org/article/44a7300a32714dd98feec844ed4f08c7