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 6
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- Image Retrieval and Classification Techniques 4
- Video Analysis and Summarization 2
- Co-authors
- Tomaž Požrl (5 shared papers)J.F. Tasič (5 shared papers)Andrej Košir (5 shared papers)Marko Tkalčič (2 shared papers)Ante Odić (2 shared papers)Vanja Subotić (4 shared papers)Mark Žic (4 shared papers)Sergei V. Pereverzyev (3 shared papers)
In The Last Decade
Matevž Kunaver
15 papers receiving 433 citations
Matevž Kunaver's Hit Papers
Peers
Comparison fields: 5 of 89
- Computational Mathematics 10
- Information Systems 301
- Management Science and Operations Research 73
- Computer Science Applications 28
- Artificial Intelligence 155
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 14 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 | 302 |
| 2 | Database for contextual personalization | 2011 | 60 |
| 3 | 2007 | 15 | |
| 4 | 2022 | 13 | |
| 5 | 2021 | 12 | |
| 6 | 2005 | 9 | |
| 7 | 2022 | 8 | |
| 8 | 2021 | 6 | |
| 9 | LDOS-CoMoDa dataset | 2012 | 5 |
| 10 | IMPROVING HUMAN-COMPUTER INTERACTION IN PERSONALIZED TV RECOMMENDER | 2012 | 4 |
| 11 | 2015 | 4 | |
| 12 | 2020 | 4 | |
| 13 | Increasing Top-20 Search Results Diversity Through Recommendation Post-Processing. | 2014 | 3 |
| 14 | 1979 | 3 | |
| 15 | 1978 | 2 | |
| 16 | 2024 | 1 | |
| 17 | 2022 | 0 |
About Matevž Kunaver
Matevž Kunaver is a scholar working on Information Systems, Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering and Management Science and Operations Research, having authored 17 papers that have together received 451 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (6 papers), Image Retrieval and Classification Techniques (4 papers), Evolutionary Algorithms and Applications (3 papers), Advanced Bandit Algorithms Research (3 papers), Metaheuristic Optimization Algorithms Research (3 papers), Electrochemical Analysis and Applications (2 papers), Mobile Crowdsensing and Crowdsourcing (2 papers) and Video Analysis and Summarization (2 papers). The work is most often cited by research in Computational Mathematics (10 citations), Information Systems (301 citations), Management Science and Operations Research (73 citations), Computer Science Applications (28 citations) and Artificial Intelligence (155 citations). Matevž Kunaver has collaborated with scholars based in Slovenia, Croatia and Austria. Frequent co-authors include Tomaž Požrl, J.F. Tasič, Andrej Košir, Marko Tkalčič, Ante Odić, Vanja Subotić, Mark Žic, Sergei V. Pereverzyev, M. Pogačnik and Branko Stanovnik. 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.