Bhaskar Mitra
- Artificial Intelligence top 1%
- Topic Modeling 30
- Natural Language Processing Techniques 13
- Advanced Text Analysis Techniques 4
- Domain Adaptation and Few-Shot Learning 4
- Information Systems top 1%
- Information Retrieval and Search Behavior 13
- Recommender Systems and Techniques 7
- Web Data Mining and Analysis 4
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- Data Quality and Management 8
- Computer Science Applications top 10%
- Co-authors
- Nick CraswellFernando DíazMilad ShokouhiFilip RadlinskiRich CaruanaEric NalisnickHamed ZamaniDaniel Campos
- Journals
- ACM SIGIR Forum (2 papers)ACM Transactions on Information Systems (1 paper)Information Retrieval (1 paper)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Bhaskar Mitra
44 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 960
- Information Systems 623
- Management Science and Operations Research 143
- Computer Vision and Pattern Recognition 234
- Computer Science Applications 39
Countries citing papers authored by Bhaskar Mitra
This map shows the geographic impact of Bhaskar Mitra'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 Bhaskar Mitra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bhaskar Mitra more than expected).
Fields of papers citing papers by Bhaskar Mitra
This network shows the impact of papers produced by Bhaskar Mitra. 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 Bhaskar Mitra. The network helps show where Bhaskar Mitra may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Bhaskar Mitra, 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 3 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 0 | |
| 6 | Large Language Models can Accurately Predict Searcher Preferencesbreakdown → | 2024 | 41 |
| 7 | 2024 | 11 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 10 | |
| 10 | 2023 | 11 | |
| 11 | 2023 | 2 | |
| 12 | 2022 | 25 | |
| 13 | 2022 | 3 | |
| 14 | 2021 | 29 | |
| 15 | 2019 | 71 | |
| 16 | 2018 | 41 | |
| 17 | 2017 | 224 | |
| 18 | 2016 | 119 | |
| 19 | A Proposal for Evaluating Answer Distillation from Web Data | 2016 | 2 |
| 20 | 2014 | 44 |
About Bhaskar Mitra
Bhaskar Mitra is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research, having authored 50 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (30 papers), Information Retrieval and Search Behavior (13 papers), Natural Language Processing Techniques (13 papers), Data Quality and Management (8 papers), Recommender Systems and Techniques (7 papers), Advanced Text Analysis Techniques (4 papers), Web Data Mining and Analysis (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). The work is most often cited by research in Artificial Intelligence (960 citations), Information Systems (623 citations) and Management Science and Operations Research (143 citations). Bhaskar Mitra has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Nick Craswell, Fernando Díaz, Milad Shokouhi, Filip Radlinski, Rich Caruana, Eric Nalisnick, Hamed Zamani, Daniel Campos, Paul Thomas and Emine Yılmaz. Their work appears in journals such as ACM SIGIR Forum, ACM Transactions on Information Systems, Information Retrieval, IEEE Transactions on Knowledge and Data Engineering and Tropical Ecology.
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.