Aleksandr Farseev
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Topic Modeling
- Sentiment Analysis and Opinion Mining
Papers in
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- Recommender Systems and Techniques 9
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- Advanced Graph Neural Networks 4
- Topic Modeling 3
- Sentiment Analysis and Opinion Mining 2
- Co-authors
- Tat‐Seng Chua (10 shared papers)Andrey Filchenkov (5 shared papers)Liqiang Nie (2 shared papers)Mohammad Akbari (2 shared papers)Sergey Nikolenko (6 shared papers)Meng Wang (1 shared paper)Luming Zhang (1 shared paper)Richang Hong (1 shared paper)
- Journals
- ACM Transactions on Information Systems (2 papers)Frontiers in Big Data (1 paper)ACM Transactions on Intelligent Systems and Technology (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (4 papers)Griffith Research Online (Griffith University, Queensland, Australia) (1 paper)
In The Last Decade
Aleksandr Farseev
19 papers receiving 268 citations
Peers
Comparison fields: 5 of 49
- Information Systems 139
- Artificial Intelligence 132
- Computer Vision and Pattern Recognition 79
- Statistical and Nonlinear Physics 46
- Transportation 25
Countries citing papers authored by Aleksandr Farseev
This map shows the geographic impact of Aleksandr Farseev'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 Aleksandr Farseev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aleksandr Farseev more than expected).
Fields of papers citing papers by Aleksandr Farseev
This network shows the impact of papers produced by Aleksandr Farseev. 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 Aleksandr Farseev. The network helps show where Aleksandr Farseev may publish in the future.
Co-authors
The 16 scholars most cited alongside Aleksandr Farseev, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 62 | |
| 2 | 2017 | 46 | |
| 3 | 2017 | 26 | |
| 4 | 2017 | 23 | |
| 5 | 2017 | 21 | |
| 6 | 2017 | 15 | |
| 7 | 2018 | 12 | |
| 8 | 2022 | 12 | |
| 9 | 2015 | 12 | |
| 10 | 2016 | 10 | |
| 11 | 2016 | 9 | |
| 12 | 2022 | 8 | |
| 13 | 2023 | 4 | |
| 14 | 2023 | 4 | |
| 15 | 2020 | 3 | |
| 16 | 2017 | 3 | |
| 17 | 2023 | 2 | |
| 18 | 2023 | 2 | |
| 19 | 2019 | 2 | |
| 20 | 2021 | 0 |
About Aleksandr Farseev
Aleksandr Farseev is a scholar working on Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science and Statistical and Nonlinear Physics, having authored 21 papers that have together received 276 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (9 papers), Complex Network Analysis Techniques (5 papers), Digital Marketing and Social Media (4 papers), Advanced Graph Neural Networks (4 papers), Video Analysis and Summarization (3 papers), Topic Modeling (3 papers), Sentiment Analysis and Opinion Mining (2 papers) and Mobile Health and mHealth Applications (2 papers). The work is most often cited by research in Information Systems (139 citations), Artificial Intelligence (132 citations), Computer Vision and Pattern Recognition (79 citations), Statistical and Nonlinear Physics (46 citations) and Transportation (25 citations). Aleksandr Farseev has collaborated with scholars based in Singapore, Russia and Australia. Frequent co-authors include Tat‐Seng Chua, Andrey Filchenkov, Liqiang Nie, Mohammad Akbari, Sergey Nikolenko, Meng Wang, Luming Zhang, Richang Hong, Denis Kotkov and Alexander Semenov. Their work appears in journals such as ACM Transactions on Information Systems, Frontiers in Big Data, ACM Transactions on Intelligent Systems and Technology, Proceedings of the AAAI Conference on Artificial Intelligence and Griffith Research Online (Griffith University, Queensland, Australia).
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.