Jonathan Gemmell
Impact in
- Information Systems top 2%
- Recommender Systems and Techniques
- Web Data Mining and Analysis
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Topic Modeling
Papers in
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- Recommender Systems and Techniques 11
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- Sentiment Analysis and Opinion Mining 5
- Advanced Graph Neural Networks 3
- Topic Modeling 3
- Co-authors
- Bamshad Mobasher (11 shared papers)Robin Burke (6 shared papers)Daniela Raicu (7 shared papers)Samah Fodeh (2 shared papers)Maryam Ramezani (1 shared paper)Ana M. León (2 shared papers)Jennifer Todd (1 shared paper)Cynthia Putnam (1 shared paper)
- Journals
- Journal of Computer and System Sciences (1 paper)Value in Health (1 paper)AI Magazine (1 paper)BMC Bioinformatics (1 paper)ACM Transactions on Accessible Computing (1 paper)
- Partner nations
- United StatesBrazilMexico
In The Last Decade
Jonathan Gemmell
26 papers receiving 561 citations
Peers
Comparison fields: 5 of 59
- Information Systems 373
- Artificial Intelligence 276
- Statistical and Nonlinear Physics 93
- Computational Mathematics 4
- Applied Psychology 30
Countries citing papers authored by Jonathan Gemmell
This map shows the geographic impact of Jonathan Gemmell'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 Jonathan Gemmell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan Gemmell more than expected).
Fields of papers citing papers by Jonathan Gemmell
This network shows the impact of papers produced by Jonathan Gemmell. 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 Jonathan Gemmell. The network helps show where Jonathan Gemmell may publish in the future.
Co-authors
The 18 scholars most cited alongside Jonathan Gemmell, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 322 | |
| 2 | 2019 | 30 | |
| 3 | 2009 | 28 | |
| 4 | 2011 | 28 | |
| 5 | 2018 | 26 | |
| 6 | 2010 | 24 | |
| 7 | 2009 | 24 | |
| 8 | 2019 | 19 | |
| 9 | 2018 | 11 | |
| 10 | 2011 | 10 | |
| 11 | A fast effective multi-channeled tag recommender | 2009 | 9 |
| 12 | 2009 | 9 | |
| 13 | 2022 | 8 | |
| 14 | 2016 | 8 | |
| 15 | 2018 | 5 | |
| 16 | 2011 | 4 | |
| 17 | 2017 | 4 | |
| 18 | 2018 | 4 | |
| 19 | 2018 | 3 | |
| 20 | 2019 | 3 |
About Jonathan Gemmell
Jonathan Gemmell is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Social Psychology and Sociology and Political Science, having authored 27 papers that have together received 588 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (11 papers), Mental Health via Writing (6 papers), Sentiment Analysis and Opinion Mining (5 papers), Advanced Graph Neural Networks (3 papers), Topic Modeling (3 papers), Complex Network Analysis Techniques (3 papers), Data Management and Algorithms (2 papers) and Opportunistic and Delay-Tolerant Networks (2 papers). The work is most often cited by research in Information Systems (373 citations), Artificial Intelligence (276 citations), Statistical and Nonlinear Physics (93 citations), Computational Mathematics (4 citations) and Applied Psychology (30 citations). Jonathan Gemmell has collaborated with scholars based in United States, Brazil and Mexico. Frequent co-authors include Bamshad Mobasher, Robin Burke, Daniela Raicu, Samah Fodeh, Maryam Ramezani, Ana M. León, Jennifer Todd, Cynthia Putnam, Andrew T. Harris and Hamed Qahri‐Saremi. Their work appears in journals such as Journal of Computer and System Sciences, Value in Health, AI Magazine, BMC Bioinformatics and ACM Transactions on Accessible Computing.
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.