Maria Maistro

546 total citations
38 papers, 119 citations indexed

About

Maria Maistro is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Maria Maistro has authored 38 papers receiving a total of 119 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 14 papers in Information Systems and 9 papers in Management Science and Operations Research. Recurrent topics in Maria Maistro's work include Topic Modeling (9 papers), Recommender Systems and Techniques (8 papers) and Information Retrieval and Search Behavior (6 papers). Maria Maistro is often cited by papers focused on Topic Modeling (9 papers), Recommender Systems and Techniques (8 papers) and Information Retrieval and Search Behavior (6 papers). Maria Maistro collaborates with scholars based in Italy, Denmark and Germany. Maria Maistro's co-authors include Nicola Ferro, Marco Ferrante, Giorgio Maria Di Nunzio, Claudio Lucchese, Tuukka Ruotsalo, Raffaele Perego, Lasse Borgholt, Alexander Junge, Lars Maaløe and Jakob D. Havtorn and has published in prestigious journals such as Information Processing & Management, ACM Transactions on Information Systems and Lecture notes in computer science.

In The Last Decade

Maria Maistro

27 papers receiving 113 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Maria Maistro Italy 7 76 49 19 17 16 38 119
Michael Völske Germany 9 189 2.5× 89 1.8× 8 0.4× 12 0.7× 16 1.0× 22 234
Xin Rong United States 4 46 0.6× 33 0.7× 12 0.6× 11 0.6× 10 0.6× 9 87
Vasileios Iosifidis Germany 5 134 1.8× 16 0.3× 6 0.3× 9 0.5× 11 0.7× 7 176
Hiroki Ouchi Japan 11 256 3.4× 53 1.1× 19 1.0× 10 0.6× 28 1.8× 35 279
Anne-Marie Vercoustre Australia 7 94 1.2× 61 1.2× 7 0.4× 15 0.9× 11 0.7× 20 127
Rianne Kaptein Netherlands 10 170 2.2× 116 2.4× 13 0.7× 5 0.3× 18 1.1× 29 219
Michael Zhu United States 5 116 1.5× 24 0.5× 13 0.7× 10 0.6× 17 1.1× 14 151
Saloni Potdar United States 7 178 2.3× 24 0.5× 6 0.3× 11 0.6× 38 2.4× 14 209
Luchen Tan Canada 9 104 1.4× 61 1.2× 11 0.6× 6 0.4× 24 1.5× 20 156
Swarnadeep Saha United States 7 201 2.6× 42 0.9× 20 1.1× 11 0.6× 35 2.2× 14 228

Countries citing papers authored by Maria Maistro

Since Specialization
Citations

This map shows the geographic impact of Maria Maistro's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Maria Maistro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Maistro more than expected).

Fields of papers citing papers by Maria Maistro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maria Maistro. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Maria Maistro. The network helps show where Maria Maistro may publish in the future.

Co-authorship network of co-authors of Maria Maistro

This figure shows the co-authorship network connecting the top 25 collaborators of Maria Maistro. A scholar is included among the top collaborators of Maria Maistro based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Maria Maistro. Maria Maistro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lioma, Christina, et al.. (2025). A Reality Check on Context Utilisation for Retrieval-Augmented Generation. Research at the University of Copenhagen (University of Copenhagen). 19691–19730.
2.
Ruotsalo, Tuukka, et al.. (2024). Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance. arXiv (Cornell University). 271–281.
3.
Bruch, Sebastian, Claudio Lucchese, Maria Maistro, & Franco Maria Nardini. (2024). Special Section on Efficiency in Neural Information Retrieval. ACM Transactions on Information Systems. 42(5). 1–4.
4.
Atanasova, Pepa, et al.. (2024). DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models. Research at the University of Copenhagen (University of Copenhagen). 14346–14360.
5.
Maistro, Maria, et al.. (2024). Toward Evaluating the Reproducibility of Information Retrieval Systems with Simulated Users. Research at the University of Copenhagen (University of Copenhagen). 25–29.
6.
Maistro, Maria, et al.. (2024). An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records. Research at the University of Copenhagen (University of Copenhagen). 4869–4890. 1 indexed citations
7.
Balog, Krisztian, et al.. (2024). Dataset and Models for Item Recommendation Using Multi-Modal User Interactions. arXiv (Cornell University). 709–718.
8.
Sakai, Tetsuya, et al.. (2023). On the Ordering of Pooled Web Pages, Gold Assessments, and Bronze Assessments. ACM Transactions on Information Systems. 42(1). 1–31. 1 indexed citations
9.
Junge, Alexander, Jakob D. Havtorn, Lasse Borgholt, et al.. (2023). Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study. arXiv (Cornell University). 2572–2582. 15 indexed citations
10.
Bruch, Sebastian, Joel Mackenzie, Maria Maistro, & Franco Maria Nardini. (2023). ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information Retrieval. Research at the University of Copenhagen (University of Copenhagen). 3456–3459. 4 indexed citations
11.
Maistro, Maria, et al.. (2022). Crowdsourcing Controller - Utilizing Reliable Agents in a Multiplayer Game. arXiv (Cornell University). 64–71. 1 indexed citations
12.
Clarke, Charles L. A., et al.. (2020). Overview of the TREC 2020 Health Misinformation Track.. Text REtrieval Conference. 5 indexed citations
13.
Ferro, Nicola, Claudio Lucchese, Maria Maistro, & Raffaele Perego. (2018). Continuation Methods and Curriculum Learning for Learning to Rank. ISTI Open Portal. 1523–1526. 4 indexed citations
14.
Spina, Damiano, Maria Maistro, Yongli Ren, et al.. (2017). Understanding user behavior in job and talent search: an initial investigation. RMIT Research Repository (RMIT University Library). 2311. 1–5. 10 indexed citations
15.
Nunzio, Giorgio Maria Di, et al.. (2017). A game of lines: Developing game mechanics for text classification. Research Padua Archive (University of Padua). 1911. 40–47. 1 indexed citations
16.
Nunzio, Giorgio Maria Di, et al.. (2016). The University of Padua (IMS) at TREC 2016 Total Recall Track.. Research Padua Archive (University of Padua). 2 indexed citations
17.
Nunzio, Giorgio Maria Di, et al.. (2016). Gamification for Machine Learning: The Classification Game.. Research Padua Archive (University of Padua). 45–52. 7 indexed citations
18.
Nunzio, Giorgio Maria Di, et al.. (2015). Unfolding Off-the-shelf IR Systems for Reproducibility. Research Padua Archive (University of Padua). 7 indexed citations
19.
Ferrante, Marco, Nicola Ferro, & Maria Maistro. (2015). Towards a Formal Framework for Utility-oriented Measurements of Retrieval Effectiveness. Padua Research Archive (University of Padova). 21–30. 17 indexed citations
20.
Ferrante, Marco, Nicola Ferro, & Maria Maistro. (2014). Injecting user models and time into precision via Markov chains. Research Padua Archive (University of Padua). 597–606. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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