EVENT REPORT

Report on the 13th Italian Information
Retrieval Workshop (IIR 2023)
Guglielmo Faggioli

Antonio Ferrara

University of Padova
Italy

Politecnico di Bari
Italy

guglielmo.faggioli@unipd.it

antonio.ferrara@poliba.it

Franco Maria Nardini

Nicola Tonellotto

ISTI-CNR
Italy

University of Pisa
Italyfrancomaria.nardini@isti.cnr.it

nicola.tonellotto@unipi.it

Abstract
The 13th Italian Information Retrieval Workshop is the thirteenth edition of the annual
conference of the Italian information retrieval and recommender systems communities. The
two days of the conference gathered interesting studies and research work on a wide range of
topics on information retrieval, recommender systems, and natural language processing, suchas Search and Ranking, Recommendation, Content Analysis, and Classification, Artificial
Intelligence, NLP, Semantics, and Dialog, Domain-Specific Applications, Human Factors
and Interfaces, and Evaluation. It was hosted by the National Research Council (CNR) of
Italy and the University of Pisa in a conference facility in Pisa, Italy.
Date: 8–9 June 2023.
Website: http://iir2023.isti.cnr.it/.

1

Introduction

The 13th Italian Information Retrieval Workshop (IIR 2023) is the thirteenth annual conferenceof the Italian information retrieval and recommender systems communities.
IIR 2023 took place on June 2023 in Pisa. The Italian Information Retrieval (IIR) Workshop
was created in 2009 at the European Summer School in Information Retrieval. Its purpose isto provide a meeting forum for stimulating and disseminating research in Information Retrieval,
where early-stage researchers (including MSc and PhD students) can network and discuss theirresearch results in an informal way, and to give them the the opportunity to meet and discusswith senior researchers. IIR is organized every year by one of the main research groups in Italy
in different locations. Previous IIR workshops took place in Milan (2022), Bari (2021), Padua(2019) Rome (2018), Lugano (2017), Venice (2016), Cagliari (2015), Rome (2014), Pisa (2013),
Bari (2012), Milan (2011), and Padua (2010).
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In 2023, the conference received 33 contributions from Italian and International institutions,with 24 papers published on its proceedings. The papers presented at the workshop covered
several different research topics, including core information retrieval and recommender systems
algorithms, privacy, fairness, domain-specific applications, NLP tasks, and evaluation.
IIR has been free of charge and relied on the hosting institutions for organizational and financial
support. IIR 2023 has been partially supported by ACM SIGIR and ISTI-CNR.

2

The Workshop

2.1

Keynotes

IIR 2023 featured two invited keynote speakers. We present the title and summary of each talk
below along with a short biography of each speaker.
Sean Macavaney — Re-Thinking Re-Ranking
Abstract Re-ranking systems take a “cascading” approach, wherein an initial candidate pool of
documents is ranked and filtered to produce a final result list. This approach exhibits a fundamental relevance misalignment problem: the most relevant documents may be filtered out by a priorstage as insufficiently relevant, ultimately reducing recall and limiting the potential effectiveness.
In this talk, I challenge the cascading paradigm by proposing methods that efficiently pull in
additional potentially-relevant documents during the re-ranking process, using the long-standing
Cluster Hypothesis. I demonstrate that these methods can improve the efficiency and effectivenessof both bi-encoder and cross-encoder retrieval models at various operational points. Cascading isdead, long live re-ranking!
Speaker’s Biography Sean Macavaney is a Lecturer in Machine Learning at the Universityof Glasgow and a member of the Terrier Team. His research primarily focuses on effective andefficient neural retrieval. He completed his PhD at Georgetown University in 2021, where he wasa member of the IR Lab and an ARCS Endowed Scholar. He was a co-recipient of the ECIR 2023
Best Short Paper award.
Christine Bauer — Three views of a secret: Embracing multiple perspectives in theevaluation of information retrieval and recommender systems
Abstract As users are increasingly confronted with information and choice overload, we needthe ‘right’ information, at the ‘right’ time, in the ‘right’ place, in the ‘right’ way, to the ‘right’
person. Information retrieval and recommender systems are effective means to address this goal.
When optimizing and evaluating such systems, we often disregard that a ‘typical’ user is not the
only stakeholder interested in a well-functioning system. Beyond ignoring the needs of specificstakeholders, this eventually leads to a malfunctioning system for anyone. In this talk, I will
demonstrate that we need to consider the demands of the various stakeholders and provide insights
into how we can embrace those needs when evaluating our systems.

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Speaker’s Biography Christine Bauer is EXDIGIT Professor for Interactive Intelligent Systems
at the Department of Artificial Intelligence and Human Interfaces (AIHI) at the Paris Lodron
University Salzburg, Austria. Her research centers on interactive intelligent systems. In recent
years, she worked on context-aware recommender systems in the music and media domains. The
core interests in her research activities are fairness and multi-method evaluations. She has authoredmore than 100 papers and holds several best paper awards and several awards for her reviewing
activities. She is on the Editorial Board of ACM Transactions on Recommender Systems (TORS)
and co-organizes the Workshop series “Perspectives on the Evaluation of Recommender Systems(PERSPECTIVES)”.

2.2

PapersThe purpose of the Italian Information Retrieval (IIR) workshop series is to provide a forum
for stimulating and disseminating research in information retrieval, where Italian researchers andresearchers affiliated with Italian institutions can network and discuss their research results in an
informal way.
The contributions to IIR 2023 mainly address the following relevant topics: Search and Ranking: Research on core Information Retrieval (IR) algorithmic topics, including IR at scale Recommendation, Content Analysis, and Classification: Research focusing on recommendersystems (RS), rich content representations and content analysis.
 Artificial Intelligence, NLP, Semantics, and Dialog: Research bridging AI and IR –, especially toward deep semantics — and dialog with intelligent agents.
 Domain-Specific Applications: Research focusing on domain-specific challenges.
 Human Factors and Interfaces: Research into user-centric aspects of IR, including userinterfaces, behavior modeling, privacy, and interactive systems.
 Evaluation: Research that focuses on the measurement and evaluation of IR systems.
 Future Directions: Research with theoretical or empirical contributions on new technical orsocial aspects of IR, especially in more speculative directions or with emerging technologies.

We received submissions of original papers presenting new research results and extended abstracts containing descriptions of ongoing projects or summarizing already published results. The
program committee involved 38 researchers, equally divided among IR and RS. At least two program committee members reviewed each submission. The reviewers looked at originality, technicaldepth, style of presentation, and impact. Finally, the committee decided to accept 5 original papers and 28 extended abstracts for presentation at the workshop. Out of these, 24 papers were
further published on the proceedings through the CEUR-WS publisher1 [Nardini et al., 2023].
The program also included two invited talks. The first talk, titled “Re-Thinking Re-Ranking”,
was given by Sean Macavaney from the University of Glasgow. The second was by Christine Bauerfrom the Paris Lodron University Salzburg and was entitled “Three views of a secret: Embracing
multiple perspectives in the evaluation of information retrieval and recommender systems”.
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http://ceur-ws.org/Vol-3448/

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2.3

Organization

2.3.1

General Chairs

Franco Maria Nardini, ISTI-CNR, Italy
Nicola Tonellotto, University of Pisa, Italy
2.3.2

Program Chairs

Guglielmo Faggioli, University of Padova, Italy
Antonio Ferrara, Polytechnic University of Bari, Italy
2.3.3

Webmaster & Graphics Design

Beatrice Rapisarda ISTI-CNR, Italy
2.3.4

Steering Committee

Aris Anagnostopoulos, University of Rome “La Sapienza”, Italy
Luca Becchetti, University of Rome “La Sapienza”, Italy
Fabio Crestani, Università della Svizzera Italiana, Switzerland
Emanuele Di Buccio, University of Padova, Italy
Massimo Melucci, University of Padova, Italy
Stefano Mizzaro, University of Udine, Italy
Tommaso Di Noia, Polytechnic University of Bari, Italy
Gabriella Pasi, University Milano Bicocca, Italy
Raffaele Perego, ISTI-CNR, Italy
Fabrizio Silvestri, University of Rome “La Sapienza”, Italy
Marko Tkalcic, Free University of Bolzano, Italy
Nicola Tonellotto, University of Pisa, Italy
Franco Maria Nardini, ISTI-CNR, Italy
Giorgio Maria Di Nunzio, University of Padova, Italy
Salvatore Orlando, University Ca’ Foscari Venezia, Italy

2.4

Program Committee

Maristella Agosti, University of Padova, Italy
Aris Anagnostopoulos, Sapienza University of Rome, Italy
Marco Angelini, University of Rome “La Sapienza”, Italy
Vito Walter Anelli, Polytechnic University of Bari, Italy
Liliana Ardissono, University of Torino, Italy
Pierpaolo Basile, University of Bari Aldo Moro, Italy
Giovanni Maria Biancofiore, Polytechnic University of Bari, Italy
Paolo Boldi, University of Milan, Italy
Ludovico Boratto, University of Cagliari, Italy
Giandomenico Cornacchia, Polytechnic University of Bari, Italy
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Paolo Cremonesi, Polytechnic University of Milano, Italy
Danilo Croce, University of Rome “Tor Vergata”, Italy
Gianluca Demartini, University of Queensland, Italy
Emanuele Di Buccio, University of Padova, Italy
Tommaso Di Noia, Polytechnic University of Bari, Italy
Giorgio Maria Di Nunzio, University of Padova, Italy
Francesco Donini, Università della Tuscia, Italy
Andrea Esuli, ISTI-CNR, Italy
Maurizio Ferrari Dacrema, Polytechnic University of Milano, Italy
Nicola Ferro, University of Padova, Italy
Claudio Lucchese, University Ca’ Foscari Venezia, Italy
Daniele Malitesta, Polytechnic University of Bari, Italy
Stefano Marchesin, University of Padova, Italy
David Massimo, Free University of Bolzano, Italy
Alberto Carlo Maria Mancino, University Polytechnic of Bari, Italy
Ida Mele, IASI-CNR, Italy
Alejandro Moreo, ISTI-CNR, Italy
Cristina Muntean, ISTI-CNR, Italy
Stefano Mizzaro, University of Udine, Italy
Raffaele Perego, ISTI-CNR, Italy
Marco Polignano, University of Bari, Italy
Claudio Pomo, Polytechnic University of Bari, Italy
Francesco Ricci, Free University of Bozen-Bolzano, Italy
Giovanni Semeraro, University of Bari “Aldo Moro”, Italy
Gianmaria Silvello, University of Padova, Italy
Marko Tkalcic, Free University of Bozen Bolzano, Italy
Salvatore Trani, ISTI-CNR, Italy
Markus Zanker, Free University of Bozen-Bolzano, Italy3

Summary

3.1

Session 1: User Satisfaction and Personalization

The initial session centered on strategies aimed at enhancing user satisfaction through the deliveryof personalized content, while also assessing potential user dissatisfaction. In this context, Fabris
et al. [2023] addressed the limitations of the family of pairwise fairness measures and introduced
a new metric, called “Dissatisfaction Induced by Pairwise Swaps,” to overcome these limitations.
Maurera et al. [2023] delved into a specific type of user-system interaction, namely impressions,and provided a comprehensive overview of their role in recommender systems from three distinct
perspectives: models, datasets, and evaluation. Massimo and Ricci [2023] detailed their reinforcement learning-based RS approach, known as QEXP, which leverages logs of Point of Interest (POI)
visits to address the cold-start problem while suggesting new POIs based on tourist movement
patterns. Braga et al. [2023] initiated their discussion by highlighting the intricacies of fine-tuninga personalized BERT model. To tackle this challenge, they introduced an approach that personACM SIGIR Forum

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alizes dialogue systems using adapter layers and topic modeling, incorporating a user embedding
vector as an external input to encode personal information into the model. Finally, Manzo et al.
[2023] addressed the context of public streaming platforms and proposed an innovative approachto personalized recommendations for streaming media content. Their model prioritizes serendipity
and multicultural diversity while minimizing the need for sharing personal data.

3.2

Session 2: Privacy, Fairness, and Explainability

The focus of the second session centered primarily on exploring fairness and explainability withinvarious information access methodologies. Several approaches investigated the fairness aspectsof recommender systems, while others provided explainable solutions to information access. In
particular, Atzori et al. [2023] examined the bias in the exposure of demographic groups in forensic
face rankings by introducing a framework comprising six face models and a public dataset. Theirresearch revealed that biases in forensic face rankings are significant, necessitating the development of specialized fairness concepts and mitigation strategies. Balloccu et al. [2023] summarized
their work on replicating and evaluating three state-of-the-art path reasoning recommendation approaches through a standardized protocol utilizing publicly available datasets. Their findings indicated that these reasoning methods, compared to knowledge-aware baselines, exhibited reduced
utility and coverage but offered increased serendipity and diversity. Di Palma et al. [2023] aimed
to address concerns related to fairness in graph-based Collaborative Filtering (CF) recommendations by proposing a taxonomy of graph CF solutions. They subsequently used this taxonomy intheir analysis, revealing that while graph CF models demonstrated superior accuracy performancebased on Consumer and Producer (CP)-fairness metrics, this came at the expense of user fairness.
Boratto et al. [2023] summarized their previous work on defining desirable properties for a fairrecommendation system in the context of recommender systems. They identified eight properties
and assessed how existing mitigation procedures aligned with these properties using two public
datasets. Afreen et al. [2023] delved into the performance of existing path reasoning methods,
specifically for educational recommendation. Afreen et al. showed promising results when thesemethods were applied to two large-scale online course datasets. Giachelle et al. [2023] extended thecapabilities of the Semantic Knowledge Extractor Tool (SKET) by introducing SKET eXplained(SKET X). This web-based system provides visual explanations that detail the models, rules, andparameters associated with each prediction, catering to pathologists and experts to facilitate thecomprehension of SKET predictions.

3.3

Session 3: NLP

The focus of the third session was on NLP at large. Among the NLP tasks addressed this year,
we recognize authorship verification, fact-checking, the development of Italian LLMs, and topic
modeling. Corbara and Moreo [2023] proposed their results to tackle the adversarial Authorship
Verification task by augmenting the training set with synthetic textual examples. Corbara andMoreo experimented with SVM and Neural Networks, and generation strategies based on language
modeling and GAN training, empirically showing that data augmentation allows for improved
performance in an adversarial setup. To obtain annotations for the fact-checking task, La Barbera
et al. [2023] investigated the possibility of employing crowd workers. Their experiments were meant
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to identify the best setting for misinformation assessment using crowdsourcing. Their results
indicated that the crowd can effectively address misinformation, at a lower cost than experts.
Bacciu et al. [2023] presented Fauno, the first and largest open-source Italian conversational Large
Language Model (LLM). Through Fauno, Bacciu et al. demonstrated the possibility of obtaining
a fine-tuned conversational bot with a single GPU is possible. Additionally, Bacciu et al. released
and described the collection of datasets for conversational AI in Italian that they used for finetuning Fauno. Ravenda et al. [2023] addressed the topic-modeling task, by proposing an approachbased on Mixtures of Normalizing Flows, which extracts topics from a collection of time-varying
documents in a dynamic way. Their experiments highlighted that the approach proposed, which
dynamically updates the topic representations is on par or better than state-of-the-art solutions.

3.4

Session 4: Efficient and Domain Specific Approaches

The fourth session was divided into two parts. The first concerned how to improve the efficiencyof the models, while the latter was focused on domain-specific approaches.
Two papers investigated how to improve the performance in the IR domain. The former
focused on accelerating the query efficiency via index decomposition, while the latter investigatedthe complexity of solving IR problems using Quantum Annealers (QA). Mackenzie et al. [2023]
introduced a novel technique to improve the query efficiency, named “postings clipping”. The
approach proposed by Mackenzie et al. differed from current efforts because every posting remains
in the original posting list, while the high-impact postings are sliced and moved to a different list.
Pellini et al. [2023] started by observing that theoretical studies indicate that specific optimization
problems are more difficult to solve on QA. Therefore, they proposed a preliminary empiricalinvestigation to recognize such hard-to-be-solved problems. Pellini et al. observed a correlation
between the features of a problem and its complexity to be solved on a QA.
Regarding domain-specific approaches, the investigated scenarios involved investigative intelligence, retrieval of clinical trials, and the generation of profiles for tourists. Campinas et al. [2023]
described how the Siren Federate plugin integrates relational search capabilities into Elasticsearchand how it allows to tackle some of the challenges in Investigative Intelligence. Di Nunzio et al.
[2023] presented their previous and current work concerning the Retrieval of Clinical Trials. Theirsolutions are based on three techniques: query reformulation, the merging of the ranking lists
obtained using summarized queries, and query expansion based on pseudo-relevance feedback. To
provide personalized tourist profiles, Merinov et al. [2023] proposed an approach to synthesize
individual-level user profiles from population-level data in the tourism domain. Merinov et al.
utilized a discrete choice behavioral model to merge choices made by the population at large withthe choices in the real data.

3.5

Session 5: Evaluation

The final session concerned the evaluation. The submitted works regarded mostly IR and included the theoretical analysis of evaluation models, the empirical assessment of LambdaMART,
the definition of a runs repository to ease experimentation, and the query performance prediction. Faggioli et al. [2023a] expanded the current evaluation strategy in IR, which is based on
General Linear Models (GLiMs), to Generalized Linear Models (GLMs), observing an improved
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power of the statistical analyses while maintaining comparable stability. Bassani [2023] introduced “ranxhub,” an online repository designed for the sharing of pre-computed runs, which are
ranked lists of documents retrieved for specific query sets by retrieval models. Ranxhub aimed to
expedite research, enhance transparency, and reduce the environmental footprint associated withexperimentation. Lucchese et al. [2023] analyzed LambdaMART gradients, presenting strategies
to mitigate gradient inconsistencies. Their approach involved computing the full gradient only
for documents that should be ranked within the top-k positions, ensuring more consistent results.
Faggioli et al. [2023b] described an approach based on the geometric properties of dense spaces toimprove Query Performance Prediction (QPP) in the conversational search domain. The model
proposed by Faggioli et al. is based on measuring the volume of the hypercuboid that encompasses
the retrieved documents.

4

Lessons Learned

The 13th Italian Information Retrieval Workshop encouraged the discussion on IR and RS fromseveral perspectives, fostering the exchange of ideas and methodologies between people working
on various research areas.
A key lesson was the significance of addressing the needs of different stakeholders when evaluating information retrieval and recommender systems. Christine Bauer’s keynote stressed theimportance of considering multiple perspectives to ensure fairness and effectiveness of the system. Moreover, the presentation by Sean Macavaney showcased novel approaches for improving
re-ranking in information retrieval challenging conventional methods, thus underscoring the needfor flexible and innovative approaches in our community.
The papers presented at the workshop covered a wide spectrum of research topics, ranging
from core information retrieval algorithms to privacy, fairness, domain-specific applications, and
NLP tasks. The presentations and the discussions offered a platform for new and experienced
researchers from the Italian IR and RS communities to learn from one another, share findings,
exchange ideas, and stay current with the latest advancements in the field.
The major takeaways are strictly connected with the importance of user-centric design andadaptability in information retrieval and recommender systems design, as well as the significanceof topics related to the trustworthiness of such systems such as privacy, fairness, explainability.
Finally, the workshop emphasized not only the need to develop new models but also the urgency
for rigorous evaluation models and resources for reproducible and easy experimentation in the fieldof Information Retrieval and Recommender Systems.
In summary, the 13th Italian Information Retrieval Workshop has once again proven itself as animportant platform for promoting collaboration, innovation, and the continuous advancement ofInformation Retrieval and Recommender Systems research, underscoring its enduring importancein our Italian community.

Acknowledgments
We thank CEUR-WS for hosting the proceedings. We thank the High-Performance Computing
Laboratory (HPC Lab) at the Information Science and Technologies Institute (ISTI) of the NaACM SIGIR Forum

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tional Research Council of Italy (CNR) and the Special Interest Group on Information Retrieval(SIGIR) for their financial support.

References
Neda Afreen, Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, and Mirko Marras. Towards
explainable educational recommendation through path reasoning methods. In Franco Maria
Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings ofthe 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages
131–136. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-29.pdf.
Andrea Atzori, Gianni Fenu, and Mirko Marras. Fairness of exposure in forensic face rankings.
In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors,
Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June
8-9, 2023, pages 91–96. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-12.
pdf.
Andrea Bacciu, Giovanni Trappolini, Andrea Santilli, Emanuele Rodolà, and Fabrizio Silvestri.
Fauno: The italian large language model that will leave you senza parole! In Franco Maria
Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings ofthe 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages
9–17. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-24.pdf.
Giacomo Balloccu, Ludovico Boratto, Christian Cancedda, Gianni Fenu, and Mirko Marras.
Knowledge-aware recommendations: Exploring the interplay between utility, explanation quality, and fairness in path reasoning methods. In Franco Maria Nardini, Nicola Tonellotto,
Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th Italian Information
Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 111–116. CEUR-WS, 2023.
URL https://ceur-ws.org/Vol-3448/paper-26.pdf.
Elias Bassani. Towards a repository for information retrieval runs. In Franco Maria Nardini,
Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th
Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 125–130.
CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-04.pdf.
Ludovico Boratto, Gianni Fenu, Mirko Marras, and Giacomo Medda. Consumer fairness benchmark in recommendation. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli,
and Antonio Ferrara, editors, Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 60–65. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-27.pdf.
Marco Braga, Alessandro Raganato, Gabriella Pasi, et al. Personalization in bert with adapter
modules and topic modelling. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli,
and Antonio Ferrara, editors, Proceedings of the 13th Italian Information Retrieval Workshop

ACM SIGIR Forum

9

Vol. 57 No. 2 – December 2023

(IIR 2023). Pisa, Italy, June 8-9, 2023, pages 24–29. CEUR-WS, 2023. URL https://ceur-w
s.org/Vol-3448/paper-13.pdf.
Stéphane Campinas, Matteo Catena, and Renaud Delbru. Relational search and its application
to investigative intelligence scenarios. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo
Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th Italian Information Retrieval
Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 117–118. CEUR-WS, 2023. URL
https://ceur-ws.org/Vol-3448/paper-28.pdf.
Silvia Corbara and Alejandro Moreo. Enhancing adversarial authorship verification with data
augmentation. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio
Ferrara, editors, Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023).
Pisa, Italy, June 8-9, 2023, pages 73–78. CEUR-WS, 2023. URL https://ceur-ws.org/Vo
l-3448/paper-11.pdf.
Giorgio Maria Di Nunzio, Guglielmo Faggioli, and Stefano Marchesin. An analysis of a methodology and experimental results for the retrieval of clinical trials. In Franco Maria Nardini,
Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th
Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 53–57.
CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-21.pdf.
Dario Di Palma, Vito Walter Anelli, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo, Yashar
Deldjoo, and Tommaso Di Noia. Examining fairness in graph-based collaborative filtering: A
consumer and producer perspective. 3448:79–84, 2023. URL https://ceur-ws.org/Vol-344
8/paper-30.pdf.
Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto, and Asia J Biega. Dissatisfaction
induced by pairwise swaps. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli,
and Antonio Ferrara, editors, Proceedings of the 13th Italian Information Retrieval Workshop(IIR 2023). Pisa, Italy, June 8-9, 2023, pages 66–71. CEUR-WS, 2023. URL https://ceur-w
s.org/Vol-3448/paper-10.pdf.
Guglielmo Faggioli, Nicola Ferro, Norbert Fuhr, et al. Ir systems evaluation via generalized linear
models. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara,
editors, Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy,
June 8-9, 2023, pages 105–110. CEUR-WS, 2023a. URL https://ceur-ws.org/Vol-3448/pa
per-02.pdf.
Guglielmo Faggioli, Nicola Ferro, Cristina Muntean, Raffaele Perego, Nicola Tonellotto, et al. A
spatial approach to predict performance of conversational search systems. In Franco Maria
Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings of the
13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, volume3448, pages 41–46. CEUR-WS, 2023b. URL https://ceur-ws.org/Vol-3448/paper-07.pdf.
Fabio Giachelle, Stefano Marchesin, and Gianmaria Silvello. Sket x: A visual analytics tool for
explaining knowledge extraction results. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo
Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th Italian Information Retrieval
ACM SIGIR Forum

10

Vol. 57 No. 2 – December 2023

Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 119–124. CEUR-WS, 2023. URL
https://ceur-ws.org/Vol-3448/paper-06.pdf.
David La Barbera, Michael Soprano, Kevin Roitero, Eddy Maddalena, Stefano Mizzaro, et al.
Fact-checking at scale with crowdsourcing: Experiments and lessons learned. In Franco Maria
Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings ofthe 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages
85–90. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-18.pdf.
Claudio Lucchese, Federico Marcuzzi, Salvatore Orlando, et al. Does lambdamart do what you
expect? In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara,
editors, Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy,
June 8-9, 2023, volume 3448, page 72. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3
448/paper-16.pdf.
Joel Mackenzie, Antonio Mallia, Alistair Moffat, and Matthias Petri. Accelerating learned sparse
indexes via term impact decomposition. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo
Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th Italian Information Retrieval
Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 47–52. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-01.pdf.
Gaetano Manzo, Yvan Pannatier, Gabriel Autès, Michaël De Lucia, Jean-Gabriel Piguet, andJean-Paul Calbimonte. Serendipity and diversity boosting for personalized streaming media
recommendation. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio
Ferrara, editors, Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023).
Pisa, Italy, June 8-9, 2023, pages 30–40. CEUR-WS, 2023. URL https://ceur-ws.org/Vo
l-3448/paper-03.pdf.
David Massimo and Francesco Ricci. Cold-start management in poi recommendation via reinforcement learning and spatial proximity exploration. In Franco Maria Nardini, Nicola Tonellotto,
Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th Italian Information
Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 58–59. CEUR-WS, 2023.
URL https://ceur-ws.org/Vol-3448/paper-09.pdf.
Fernando B Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, and Paolo Cremonesi.
Impressions in recommender systems: Present and future. In Franco Maria Nardini, Nicola
Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th ItalianInformation Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, volume 3448, pages
97–104. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-23.pdf.
Pavel Merinov, David Massimo, and Francesco Ricci. Behaviour-aware tourist profiles data generation. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara,
editors, Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy,
June 8-9, 2023, pages 3–8. CEUR-WS, 2023. URL https://ceur-ws.org/Vol-3448/paper-0
8.pdf.

ACM SIGIR Forum

11

Vol. 57 No. 2 – December 2023

Franco Maria Nardini, Nicola Tonellotto, Guglielmo Faggioli, and Antonio Ferrara, editors. Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9,2023, volume 3448, 2023. CEUR-WS. URL http://ceur-ws.org/Vol-3448/.
Riccardo Pellini, M Ferrari Dacrema, Paolo Cremonesi, et al. Towards improved qubo formulationsof ir tasks for quantum annealers. In Franco Maria Nardini, Nicola Tonellotto, Guglielmo
Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th Italian Information Retrieval
Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 137–142. CEUR-WS, 2023. URL
https://ceur-ws.org/Vol-3448/paper-05.pdf.
Federico Ravenda, Andrea Raballo, Antonietta Mira, and Fabio Crestani. Incremental mixtureof normalizing flows for dynamic topic modelling. In Franco Maria Nardini, Nicola Tonellotto,
Guglielmo Faggioli, and Antonio Ferrara, editors, Proceedings of the 13th Italian Information
Retrieval Workshop (IIR 2023). Pisa, Italy, June 8-9, 2023, pages 18–23. CEUR-WS, 2023.
URL https://ceur-ws.org/Vol-3448/paper-19.pdf.

ACM SIGIR Forum

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