István Pilászy

1.3k total citations
10 papers, 838 citations indexed

About

István Pilászy is a scholar working on Information Systems, Artificial Intelligence and Marketing. According to data from OpenAlex, István Pilászy has authored 10 papers receiving a total of 838 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Information Systems, 5 papers in Artificial Intelligence and 4 papers in Marketing. Recurrent topics in István Pilászy's work include Recommender Systems and Techniques (10 papers), Text and Document Classification Technologies (3 papers) and Customer churn and segmentation (3 papers). István Pilászy is often cited by papers focused on Recommender Systems and Techniques (10 papers), Text and Document Classification Technologies (3 papers) and Customer churn and segmentation (3 papers). István Pilászy collaborates with scholars based in Hungary and Germany. István Pilászy's co-authors include Domonkos Tikk, G. Takács and Bottyán Németh and has published in prestigious journals such as Journal of Machine Learning Research and ACM SIGKDD Explorations Newsletter.

In The Last Decade

István Pilászy

10 papers receiving 784 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
István Pilászy Hungary	9	661	378	298	155	106	10	838
Bottyán Németh Hungary	6	466 0.7×	268 0.7×	222 0.7×	102 0.7×	86 0.8×	10	617
Yu-Chin Juan Taiwan	6	469 0.7×	337 0.9×	296 1.0×	84 0.5×	84 0.8×	7	676
Jinoh Oh South Korea	14	812 1.2×	662 1.8×	324 1.1×	133 0.9×	153 1.4×	30	1.1k
Wei-Sheng Chin Taiwan	5	431 0.7×	321 0.8×	266 0.9×	78 0.5×	79 0.7×	6	625
Josh Attenberg United States	12	274 0.4×	536 1.4×	228 0.8×	73 0.5×	165 1.6×	20	865
Georges Dupret United States	14	553 0.8×	283 0.7×	142 0.5×	78 0.5×	79 0.7×	28	818
Weihong Wang China	10	270 0.4×	171 0.5×	184 0.6×	50 0.3×	123 1.2×	45	549
Xiwang Yang China	9	841 1.3×	504 1.3×	192 0.6×	133 0.9×	291 2.7×	15	1.1k
Wensi Xi United States	10	810 1.2×	510 1.3×	292 1.0×	69 0.4×	168 1.6×	17	1.1k
Dmitry Pavlov United States	11	254 0.4×	250 0.7×	95 0.3×	55 0.4×	55 0.5×	16	487

Countries citing papers authored by István Pilászy

Since Specialization

Citations

This map shows the geographic impact of István Pilászy'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 István Pilászy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites István Pilászy more than expected).

Fields of papers citing papers by István Pilászy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by István Pilászy. 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 István Pilászy. The network helps show where István Pilászy may publish in the future.

Co-authorship network of co-authors of István Pilászy

This figure shows the co-authorship network connecting the top 25 collaborators of István Pilászy. A scholar is included among the top collaborators of István Pilászy 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 István Pilászy. István Pilászy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Németh, Bottyán, G. Takács, István Pilászy, & Domonkos Tikk. (2013). Visualization of movie features in collaborative filtering. 10. 229–233. 5 indexed citations

2.

Takács, G., István Pilászy, & Domonkos Tikk. (2011). Applications of the conjugate gradient method for implicit feedback collaborative filtering. 297–300. 30 indexed citations

3.

Pilászy, István, et al.. (2010). Fast als-based matrix factorization for explicit and implicit feedback datasets. 71–78. 118 indexed citations

4.

Takács, G., István Pilászy, Bottyán Németh, & Domonkos Tikk. (2009). Scalable Collaborative Filtering Approaches for Large Recommender Systems. Journal of Machine Learning Research. 10(22). 623–656. 305 indexed citations

5.

Pilászy, István & Domonkos Tikk. (2009). Recommending new movies. 93–100. 81 indexed citations

6.

Takács, G., István Pilászy, Bottyán Németh, & Domonkos Tikk. (2008). Matrix factorization and neighbor based algorithms for the netflix prize problem. 267–274. 108 indexed citations

7.

Takács, G., István Pilászy, Bottyán Németh, & Domonkos Tikk. (2008). Investigation of Various Matrix Factorization Methods for Large Recommender Systems. 553–562. 38 indexed citations

8.

Takács, G., István Pilászy, Bottyán Németh, & Domonkos Tikk. (2008). Investigation of various matrix factorization methods for large recommender systems. 1–8. 73 indexed citations

9.

Takács, G., István Pilászy, Bottyán Németh, & Domonkos Tikk. (2008). A unified approach of factor models and neighbor based methods for large recommender systems. 17. 186–191. 8 indexed citations

10.

Takács, G., István Pilászy, Bottyán Németh, & Domonkos Tikk. (2007). Major components of the gravity recommendation system. ACM SIGKDD Explorations Newsletter. 9(2). 80–83. 72 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.

Explore authors with similar magnitude of impact