Guglielmo Faggioli

Guglielmo Faggioli
University of Padova | UNIPD · Department of Information Engineering

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21
Publications
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53
Citations

Publications

Publications (21)
Chapter
Amyotrophic Lateral Sclerosis (ALS) is a severe chronic disease characterized by progressive or alternate impairment of neurological functions, characterized by high heterogeneity both in symptoms and disease progression. As a consequence its clinical course is highly uncertain, challenging both patients and clinicians. Indeed, patients have to man...
Article
Full-text available
Query performance prediction (QPP) has been studied extensively in the IR community over the last two decades. A by-product of this research is a methodology to evaluate the effectiveness of QPP techniques. In this paper, we re-examine the existing evaluation methodology commonly used for QPP, and propose a new approach. Our key idea is to model QP...
Article
Full-text available
Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally ex...
Preprint
Full-text available
Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally ex...
Chapter
The rapid growth in the number and complexity of conversational agents has highlighted the need for suitable evaluation tools to describe their performance. The main evaluation paradigms move from analyzing conversations where the user explores information needs following a scripted dialogue with the agent. We argue that this is not a realistic set...
Article
The crucial role played by interpretability in many practical scenarios has led a large part of the research on machine learning towards the development of interpretable approaches. In this work, we present PRL, a game-theory-based method capable of achieving state-of-the-art accuracy, yet keeping the focus on the interpretability of the prediction...
Article
This is a report on the eleventh edition of the Conference and Labs of the Evaluation Forum (CLEF 2021), (virtually) held on September 21--24, 2021, in Bucharest, Romania. CLEF was a four day event combining a Conference and an Evaluation Forum. The Conference featured keynotes by Naila Murray and Mark Sanderson, and presentation of peer reviewed r...
Article
Full-text available
Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision within a reasonable time frame has become an important issue. In t...
Article
Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In rece...
Chapter
The ultimate goal of the evaluation is to understand when two IR systems are (significantly) different. To this end, many comparison procedures have been developed over time. However, to date, most reproducibility efforts focused just on reproducing systems and algorithms, almost fully neglecting to investigate the reproducibility of the methods we...
Conference Paper
Full-text available
The ultimate goal of the evaluation is to understand when two IR systems are (significantly) different. To this end, many comparison procedures have been developed over time. However, to date, most re-producibility efforts focused just on reproducing systems and algorithms, almost fully neglecting to investigate the reproducibility of the methods w...
Conference Paper
Full-text available
Query Performance Prediction (QPP) has been studied extensively in the IR community over the last two decades. A by-product of this research is a methodology to evaluate the effectiveness of QPP techniques. In this paper, we reexamine the existing evaluation methodology commonly used for QPP, and propose a new approach. Our key idea is to model QPP...
Book
This book constitutes the refereed proceedings of the 12th International Conference of the CLEF Association, CLEF 2021, held virtually in September 2021. The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured...
Chapter
We propose a large margin preference learning model based on game theory to solve the label ranking problem. Specifically, we show the proposed formulation is able to perform multiclass classification by solving a single convex optimization problem. Generally, such formulation, although theoretically well-founded, requires to learn a large number o...
Chapter
In the web 2.0 era, tags provide an effective mechanism to rapidly annotate and categorize items. However, tags suffer from many problems typically linked to language, like synonymy, polysemy, and ambiguity in general. To overcome this limitation, tag clustering can be used to group tags that represent similar concepts. One of the domains where tag...
Conference Paper
As already pointed out by a constantly growing literature, explainability in recommender systems field is a key aspect to increase users' satisfaction. With the increase of user generated content, tags have proven to be highly relevant when it comes to describe either users or items. A number of strategies that rely on tags have been proposed, yet,...
Chapter
Full-text available
Nowadays large scale Knowledge Bases (KBs) represent very important resources when it comes to develop expert systems. However, despite their huge sizes, KBs often suffer from incompleteness. Recently, much effort has been devoted in developing learning models to reduce the aforementioned issue. In this work, we show how relational learning tasks,...
Conference Paper
Full-text available
In this paper, the pipeline we used in the RecSys challenge 2018 is reported. We present content-based and collaborative filtering approaches for the definition of the similarity matrices for top-500 recommendation task. In particular, the task consisted in recommending songs to add to partial playlists. Different methods have been proposed dependi...

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