Fabio Clarizia
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- Online Learning and Analytics 2
- Artificial Intelligence top 10%
- Advanced Text Analysis Techniques 8
- Sentiment Analysis and Opinion Mining 4
- Topic Modeling 4
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- Service and Product Innovation 3
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- Context-Aware Activity Recognition Systems 4
- Information Systems top 10%
- Web Data Mining and Analysis 3
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- Human Mobility and Location-Based Analysis 3
- Co-authors
- Marco LombardiFrancesco ColaceMassimo De SantoDomenico SantanielloFrancesco PascaleMario CasilloGiuseppe D’AnielloLuca Carrubbo
- Journals
- Pattern Recognition Letters (1 paper)Concurrency and Computation Practice and Experience (1 paper)Information (1 paper)
- Partner nations
- ItalyUnited KingdomAlbania
In The Last Decade
Fabio Clarizia
23 papers receiving 219 citations
Peers
Comparison fields: 5 of 62
- Computer Science Applications 19
- Artificial Intelligence 106
- Marketing 28
- Computer Vision and Pattern Recognition 47
- Information Systems 51
Countries citing papers authored by Fabio Clarizia
This map shows the geographic impact of Fabio Clarizia'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 Fabio Clarizia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabio Clarizia more than expected).
Fields of papers citing papers by Fabio Clarizia
This network shows the impact of papers produced by Fabio Clarizia. 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 Fabio Clarizia. The network helps show where Fabio Clarizia may publish in the future.
Co-authorship network
The 22 scholars most cited alongside Fabio Clarizia, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 4 | |
| 2 | 2020 | 49 | |
| 3 | 2019 | 21 | |
| 4 | 2019 | 18 | |
| 5 | 2019 | 25 | |
| 6 | 2019 | 5 | |
| 7 | 2019 | 1 | |
| 8 | 2018 | 8 | |
| 9 | 2018 | 1 | |
| 10 | 2017 | 14 | |
| 11 | 2016 | 11 | |
| 12 | Advances in Service Research for the Understanding and the Management of Service in Healthcare Networks | 2013 | 6 |
| 13 | Service Research Contribution to healthcare networks understanding | 2012 | 5 |
| 14 | Improving Text Retrieval Accuracy Using a Graph of Terms | 2011 | 1 |
| 15 | 2011 | 7 | |
| 16 | 2011 | 4 | |
| 17 | 2010 | 1 | |
| 18 | 2010 | 3 | |
| 19 | 2009 | 2 | |
| 20 | 2009 | 1 |
About Fabio Clarizia
Fabio Clarizia is a scholar working on Transportation, Artificial Intelligence and Computer Science Applications, having authored 24 papers that have together received 227 indexed citations. Recurring topics across this work include Advanced Text Analysis Techniques (8 papers), Sentiment Analysis and Opinion Mining (4 papers), Topic Modeling (4 papers), Context-Aware Activity Recognition Systems (4 papers), Web Data Mining and Analysis (3 papers), Service and Product Innovation (3 papers), Human Mobility and Location-Based Analysis (3 papers) and Online Learning and Analytics (2 papers). The work is most often cited by research in Computer Science Applications (19 citations), Artificial Intelligence (106 citations) and Marketing (28 citations). Fabio Clarizia has collaborated with scholars based in Italy, United Kingdom and Albania. Frequent co-authors include Marco Lombardi, Francesco Colace, Massimo De Santo, Domenico Santaniello, Francesco Pascale, Mario Casillo, Giuseppe D’Aniello, Luca Carrubbo, Antonio Pietrosanto and Paolo Napoletano. Their work appears in journals such as Pattern Recognition Letters, Concurrency and Computation Practice and Experience and Information.
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.