Vineeth Rakesh
Impact in
- Management Information Systems top 10%
- FinTech, Crowdfunding, Digital Finance
- Information Systems top 5%
- Recommender Systems and Techniques
Papers in
-
- Topic Modeling 5
- Natural Language Processing Techniques 2
- Advanced Text Analysis Techniques 2
- Sentiment Analysis and Opinion Mining 2
-
- Recommender Systems and Techniques 4
- Co-authors
- Chandan K. Reddy (9 shared papers)Wang-Chien Lee (1 shared paper)Dilpreet Singh (2 shared papers)Suhang Wang (2 shared papers)Kai Shu (1 shared paper)Huan Liu (1 shared paper)Alexander Kotov (1 shared paper)Bhanukiran Vinzamuri (1 shared paper)
- Journals
- ACM Transactions on Knowledge Discovery from Data (1 paper)Proceedings of the International AAAI Conference on Web and Social Media (2 papers)Proceedings of the 31st ACM International Conference on Information & Knowledge Management (1 paper)
- Partner nations
- United States
In The Last Decade
Vineeth Rakesh
14 papers receiving 257 citations
Peers
Comparison fields: 5 of 42
- Management Information Systems 81
- Information Systems 133
- Transportation 36
- Artificial Intelligence 110
- Statistical and Nonlinear Physics 39
Countries citing papers authored by Vineeth Rakesh
This map shows the geographic impact of Vineeth Rakesh'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 Vineeth Rakesh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vineeth Rakesh more than expected).
Fields of papers citing papers by Vineeth Rakesh
This network shows the impact of papers produced by Vineeth Rakesh. 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 Vineeth Rakesh. The network helps show where Vineeth Rakesh may publish in the future.
Co-authors
The 25 scholars most cited alongside Vineeth Rakesh, 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 | 2016 | 69 | |
| 2 | 2016 | 49 | |
| 3 | 2019 | 27 | |
| 4 | 2014 | 23 | |
| 5 | 2017 | 22 | |
| 6 | 2018 | 20 | |
| 7 | 2013 | 17 | |
| 8 | 2018 | 15 | |
| 9 | 2021 | 10 | |
| 10 | 2022 | 5 | |
| 11 | 2019 | 4 | |
| 12 | 2018 | 4 | |
| 13 | 2025 | 2 | |
| 14 | 2022 | 1 | |
| 15 | 2023 | 0 |
About Vineeth Rakesh
Vineeth Rakesh is a scholar working on Artificial Intelligence, Information Systems, Management Information Systems, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 15 papers that have together received 268 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Recommender Systems and Techniques (4 papers), FinTech, Crowdfunding, Digital Finance (3 papers), Data Management and Algorithms (2 papers), Natural Language Processing Techniques (2 papers), Caching and Content Delivery (2 papers), Advanced Text Analysis Techniques (2 papers) and Sentiment Analysis and Opinion Mining (2 papers). The work is most often cited by research in Management Information Systems (81 citations), Information Systems (133 citations), Transportation (36 citations), Artificial Intelligence (110 citations) and Statistical and Nonlinear Physics (39 citations). Vineeth Rakesh has collaborated with scholars based in United States. Frequent co-authors include Chandan K. Reddy, Wang-Chien Lee, Dilpreet Singh, Suhang Wang, Kai Shu, Huan Liu, Alexander Kotov, Bhanukiran Vinzamuri, Raha Moraffah and Ruocheng Guo. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, Proceedings of the International AAAI Conference on Web and Social Media and Proceedings of the 31st ACM International Conference on Information & Knowledge Management.
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.