Matevž Kunaver

711 total citations · 1 hit paper
17 papers, 451 citations indexed

About

Matevž Kunaver is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Matevž Kunaver has authored 17 papers receiving a total of 451 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Information Systems, 5 papers in Computer Vision and Pattern Recognition and 5 papers in Artificial Intelligence. Recurrent topics in Matevž Kunaver's work include Recommender Systems and Techniques (6 papers), Image Retrieval and Classification Techniques (4 papers) and Metaheuristic Optimization Algorithms Research (3 papers). Matevž Kunaver is often cited by papers focused on Recommender Systems and Techniques (6 papers), Image Retrieval and Classification Techniques (4 papers) and Metaheuristic Optimization Algorithms Research (3 papers). Matevž Kunaver collaborates with scholars based in Slovenia, Austria and Croatia. Matevž Kunaver's co-authors include Tomaž Požrl, J.F. Tasič, Andrej Košir, Ante Odić, Marko Tkalčič, Mark Žic, Vanja Subotić, Sergei V. Pereverzyev, M. Pogačnik and Árpád Bűrmen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of The Electrochemical Society and Tetrahedron Letters.

In The Last Decade

Matevž Kunaver

15 papers receiving 433 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Diversity in recommender systems – A survey

2017 302 citations Matevž Kunaver, Tomaž Požrl Knowledge-Based Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Matevž Kunaver Slovenia	7	301	155	78	73	71	17	451
Fernando Díez Spain	9	347 1.2×	156 1.0×	96 1.2×	89 1.2×	50 0.7×	33	462
Justin Basilico United States	9	279 0.9×	181 1.2×	111 1.4×	94 1.3×	41 0.6×	14	432
Jinmook Kim United States	7	296 1.0×	121 0.8×	85 1.1×	56 0.8×	38 0.5×	13	384
Ana Peleteiro Spain	10	281 0.9×	99 0.6×	109 1.4×	62 0.8×	125 1.8×	18	423
Simon Dooms Belgium	13	357 1.2×	157 1.0×	126 1.6×	63 0.9×	63 0.9×	25	426
Tim Donkers Germany	8	301 1.0×	292 1.9×	93 1.2×	70 1.0×	48 0.7×	19	469
Anna Maclachlan Canada	6	354 1.2×	175 1.1×	127 1.6×	54 0.7×	66 0.9×	8	478
Marta Rey-López Spain	9	325 1.1×	116 0.7×	132 1.7×	32 0.4×	117 1.6×	16	445
Benedikt Loepp Germany	8	332 1.1×	230 1.5×	126 1.6×	83 1.1×	39 0.5×	22	432
Jiahui Liu China	6	365 1.2×	234 1.5×	128 1.6×	71 1.0×	57 0.8×	18	508

Countries citing papers authored by Matevž Kunaver

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Matevž Kunaver

This figure shows the co-authorship network connecting the top 25 collaborators of Matevž Kunaver. A scholar is included among the top collaborators of Matevž Kunaver based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matevž Kunaver. Matevž Kunaver is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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17 of 17 papers shown

Bűrmen, Árpád, et al.. (2024). Imperative Genetic Programming. Symmetry. 16(9). 1146–1146. 1 indexed citations

Žic, Mark, et al.. (2022). Extraction of Distribution Function of Relaxation Times by using Levenberg-Marquardt Algorithm: A New Approach to Apply a Discretization Error Free Jacobian Matrix. Journal of The Electrochemical Society. 169(3). 30508–30508. 13 indexed citations

Kunaver, Matevž, et al.. (2022). Automatic Grammatical Evolution-Based Optimization of Matrix Factorization Algorithm. Mathematics. 10(7). 1139–1139.

Kunaver, Matevž, et al.. (2022). Extraction of Distribution Function of Relaxation Times by using DRT-RBLM Tools: A New Approach to Combine Levenberg-Marquardt Algorithm and Radial Basis Functions for Discretization Basis. Journal of The Electrochemical Society. 169(11). 110529–110529. 8 indexed citations

Kunaver, Matevž, et al.. (2021). Synthesizing Electrically Equivalent Circuits for Use in Electrochemical Impedance Spectroscopy through Grammatical Evolution. Processes. 9(11). 1859–1859. 6 indexed citations

Žic, Mark, et al.. (2021). Investigation of Electrochemical Processes in Solid Oxide Fuel Cells by Modified Levenberg–Marquardt Algorithm: A New Automatic Update Limit Strategy. Processes. 9(1). 108–108. 12 indexed citations

Kunaver, Matevž. (2020). Grammatical Evolution-based Analog Circuit Synthesis. SHILAP Revista de lepidopterología. 4 indexed citations

Kunaver, Matevž & Tomaž Požrl. (2017). Diversity in recommender systems – A survey. Knowledge-Based Systems. 123. 154–162. 302 indexed citations breakdown →

Kunaver, Matevž, et al.. (2015). Using latent features to measure the diversity of recommendation lists. 25. 1230–1234. 4 indexed citations

10.

Kunaver, Matevž, et al.. (2014). Increasing Top-20 Search Results Diversity Through Recommendation Post-Processing.. 3 indexed citations

11.

Požrl, Tomaž, Matevž Kunaver, M. Pogačnik, Andrej Košir, & J.F. Tasič. (2012). IMPROVING HUMAN-COMPUTER INTERACTION IN PERSONALIZED TV RECOMMENDER. 36(1). 19–36. 4 indexed citations

12.

Odić, Ante, Marko Tkalčič, Matevž Kunaver, et al.. (2012). LDOS-CoMoDa dataset. Repository of the University of Ljubljana (University of Ljubljana). 5 indexed citations

13.

Košir, Andrej, Ante Odić, Matevž Kunaver, Marko Tkalčič, & J.F. Tasič. (2011). Database for contextual personalization. 60 indexed citations

14.

Kunaver, Matevž, Tomaž Požrl, M. Pogačnik, & J.F. Tasič. (2007). Optimisation of combined collaborative recommender systems. AEU - International Journal of Electronics and Communications. 61(7). 433–443. 15 indexed citations

15.

Kunaver, Matevž & J.F. Tasič. (2005). Image feature extraction - an overview. 183–186. 9 indexed citations

16.

Stanovnik, Branko, et al.. (1979). An attempt to convert heterocyclic amines into azides by an aza‐transfer reaction under phase‐transfer reaction conditions. Journal of Heterocyclic Chemistry. 16(8). 1567–1570. 3 indexed citations

17.

Stanovnik, Branko, et al.. (1978). AZA-transfer reactions a novel method for the conversion of monosubstituted hydrazino compounds into hydrocarbons. Tetrahedron Letters. 19(33). 3059–3062. 2 indexed citations

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.

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