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Franco Maria Nardini

Researcher at Istituto di Scienza e Tecnologie dell'Informazione

Publications -  117
Citations -  1615

Franco Maria Nardini is an academic researcher from Istituto di Scienza e Tecnologie dell'Informazione. The author has contributed to research in topics: Ranking (information retrieval) & Learning to rank. The author has an hindex of 18, co-authored 102 publications receiving 1154 citations. Previous affiliations of Franco Maria Nardini include National Research Council.

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Journal ArticleDOI

On planning sightseeing tours with TripBuilder

TL;DR: Experimental results on three different cities show that the proposed unsupervised framework for planning personalized sightseeing tours in cities is effective, efficient and outperforms competitive baselines.
Proceedings ArticleDOI

Where shall we go today?: planning touristic tours with tripbuilder

TL;DR: This paper mines from Flickr the information about the actual itineraries followed by a multitude of different tourists, and matches these itineraries on the touristic Point of Interests available from Wikipedia, to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget.
Proceedings ArticleDOI

Efficient Document Re-Ranking for Transformers by Precomputing Term Representations

TL;DR: The proposed approach, called PreTTR (Precomputing Transformer Term Representations), considerably reduces the query-time latency of deep transformer networks making these networks more practical to use in a real-time ranking scenario.
Journal ArticleDOI

Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees

TL;DR: QuickScorer is presented, a new algorithm that adopts a novel cache-efficient representation of a given tree ensemble, performs an interleaved traversal by means of fast bitwise operations, and supports ensembles of oblivious trees.
Posted Content

Efficient Diversification of Web Search Results

TL;DR: In this article, a unified framework for studying performance and feasibility of result diversification solutions is proposed, and a new methodology for detecting when, and how, query results need to be diversified is defined.