Maria Maistro
- Artificial Intelligence
- Information Systems top 10%
- Management Science and Operations Research
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Co-authors
- Nicola FerroMarco FerranteGiorgio Maria Di NunzioRaffaele PeregoClaudio LuccheseTuukka RuotsaloJakob D. HavtornLasse Borgholt
- Topics
- Topic Modeling (9 papers)Recommender Systems and Techniques (8 papers)Information Retrieval and Search Behavior (6 papers)
In The Last Decade
Maria Maistro
27 papers receiving 113 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 76
- Information Systems 49
- Management Science and Operations Research 19
- Computer Science Applications 17
- Computer Vision and Pattern Recognition 16
Countries citing papers authored by Maria Maistro
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 1 | |
| 10 | 4 | |
| 11 | 15 | |
| 12 | Overview of the TREC 2020 Health Misinformation Track. | 5 |
| 13 | 4 | |
| 14 | Understanding user behavior in job and talent search: an initial investigation | 10 |
| 15 | A game of lines: Developing game mechanics for text classification | 1 |
| 16 | The University of Padua (IMS) at TREC 2016 Total Recall Track. | 2 |
| 17 | Gamification for Machine Learning: The Classification Game. | 7 |
| 18 | Unfolding Off-the-shelf IR Systems for Reproducibility | 7 |
| 19 | 17 | |
| 20 | 10 |
About Maria Maistro
Maria Maistro is a scholar working on Computer Science Applications, Management Science and Operations Research and Artificial Intelligence, having authored 38 papers that have together received 119 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Recommender Systems and Techniques (8 papers) and Information Retrieval and Search Behavior (6 papers). The work is most often cited by research in Computer Science Applications (17 citations), Artificial Intelligence (76 citations) and Health Informatics (3 citations). Maria Maistro has collaborated with scholars based in Italy, Denmark and Germany. Frequent co-authors include Nicola Ferro, Marco Ferrante, Giorgio Maria Di Nunzio, Raffaele Perego, Claudio Lucchese, Tuukka Ruotsalo, Jakob D. Havtorn, Lasse Borgholt, Alexander Junge and Lars Maaløe. Their work appears in journals such as Information Processing & Management, ACM Transactions on Information Systems and Lecture notes in computer science.
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.