Matevž Kunaver
Impact in
- Computational Mathematics top 10%
- Information Systems top 2%
- Recommender Systems and Techniques
Papers in
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- Recommender Systems and Techniques 8
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- Evolutionary Algorithms and Applications 3
- Metaheuristic Optimization Algorithms Research 3
- Co-authors
- Tomaž Požrl (6 shared papers)J.F. Tasič (5 shared papers)Andrej Košir (6 shared papers)Ante Odić (2 shared papers)Marko Tkalčič (2 shared papers)Sergei V. Pereverzyev (3 shared papers)Vanja Subotić (4 shared papers)Mark Žic (4 shared papers)
In The Last Decade
Matevž Kunaver
17 papers receiving 504 citations
Matevž Kunaver's Hit Papers
Peers
Comparison fields: 5 of 94
- Computational Mathematics 10
- Information Systems 354
- Management Science and Operations Research 90
- Marketing 54
- Artificial Intelligence 191
Countries citing papers authored by Matevž Kunaver
This map shows the geographic impact of Matevž Kunaver'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 Matevž Kunaver with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matevž Kunaver more than expected).
Fields of papers citing papers by Matevž Kunaver
This network shows the impact of papers produced by Matevž Kunaver. 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 Matevž Kunaver. The network helps show where Matevž Kunaver may publish in the future.
Co-authors
The 15 scholars most cited alongside Matevž Kunaver, 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 | Diversity in recommender systems – A survey Hit paper breakdown → | 2017 | 354 |
| 2 | Database for contextual personalization | 2011 | 78 |
| 3 | 2007 | 16 | |
| 4 | 2022 | 15 | |
| 5 | 2021 | 12 | |
| 6 | 2005 | 10 | |
| 7 | 2022 | 8 | |
| 8 | 2021 | 6 | |
| 9 | LDOS-CoMoDa dataset | 2012 | 6 |
| 10 | 1979 | 5 | |
| 11 | IMPROVING HUMAN-COMPUTER INTERACTION IN PERSONALIZED TV RECOMMENDER | 2012 | 5 |
| 12 | 2015 | 5 | |
| 13 | 2014 | 5 | |
| 14 | 1978 | 4 | |
| 15 | 2020 | 4 | |
| 16 | Increasing Top-20 Search Results Diversity Through Recommendation Post-Processing. | 2014 | 3 |
| 17 | 2016 | 3 | |
| 18 | 2024 | 1 | |
| 19 | 2022 | 0 |
About Matevž Kunaver
Matevž Kunaver is a scholar working on Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research and Electrical and Electronic Engineering, having authored 19 papers that have together received 540 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (8 papers), Image Retrieval and Classification Techniques (4 papers), Advanced Bandit Algorithms Research (4 papers), Evolutionary Algorithms and Applications (3 papers), Metaheuristic Optimization Algorithms Research (3 papers), Video Analysis and Summarization (2 papers), Mobile Crowdsensing and Crowdsourcing (2 papers) and Electrochemical Analysis and Applications (2 papers). The work is most often cited by research in Computational Mathematics (10 citations), Information Systems (354 citations), Management Science and Operations Research (90 citations), Marketing (54 citations) and Artificial Intelligence (191 citations). Matevž Kunaver has collaborated with scholars based in Slovenia, Austria and Croatia. Frequent co-authors include Tomaž Požrl, J.F. Tasič, Andrej Košir, Ante Odić, Marko Tkalčič, Sergei V. Pereverzyev, Vanja Subotić, Mark Žic, M. Pogačnik and M. TIŠLER. Their work appears in journals such as Journal of The Electrochemical Society, Tetrahedron Letters, Knowledge-Based Systems, Symmetry and Processes.
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.