Rolf Jagerman

442 total citations
15 papers, 142 citations indexed

About

Rolf Jagerman is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Rolf Jagerman has authored 15 papers receiving a total of 142 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 3 papers in Information Systems and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Rolf Jagerman's work include Topic Modeling (8 papers), Natural Language Processing Techniques (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Rolf Jagerman is often cited by papers focused on Topic Modeling (8 papers), Natural Language Processing Techniques (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Rolf Jagerman collaborates with scholars based in United States, Netherlands and Italy. Rolf Jagerman's co-authors include Maarten de Rijke, Xuanhui Wang, Michael Bendersky, Ilya Markov, Honglei Zhuang, Zhen Qin, Kai Hui, Jialu Liu, Jiaming Shen and Krisztian Balog and has published in prestigious journals such as Journal of Data and Information Quality, Data Archiving and Networked Services (DANS) and UvA-DARE (University of Amsterdam).

In The Last Decade

Rolf Jagerman

13 papers receiving 139 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rolf Jagerman United States 6 93 68 31 24 10 15 142
Guglielmo Faggioli Italy 8 135 1.5× 99 1.5× 38 1.2× 23 1.0× 12 1.2× 30 204
Yancheng He China 8 114 1.2× 69 1.0× 14 0.5× 28 1.2× 8 0.8× 16 167
Deqing Yang China 9 177 1.9× 120 1.8× 28 0.9× 35 1.5× 8 0.8× 34 232
Kai Hui Germany 8 228 2.5× 103 1.5× 21 0.7× 46 1.9× 3 0.3× 25 288
Quan Lu China 8 165 1.8× 55 0.8× 35 1.1× 23 1.0× 25 2.5× 24 229
Sarik Ghazarian United States 6 137 1.5× 93 1.4× 13 0.4× 46 1.9× 9 0.9× 11 202
Makbule Gülçin Özsoy Türkiye 7 174 1.9× 73 1.1× 21 0.7× 12 0.5× 3 0.3× 12 225
Jinze Bai China 3 118 1.3× 154 2.3× 37 1.2× 50 2.1× 4 0.4× 4 183
Yiheng Shu China 2 92 1.0× 118 1.7× 34 1.1× 28 1.2× 3 0.3× 3 140
Keping Bi China 5 107 1.2× 93 1.4× 8 0.3× 33 1.4× 5 0.5× 14 153

Countries citing papers authored by Rolf Jagerman

Since Specialization
Citations

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

Fields of papers citing papers by Rolf Jagerman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rolf Jagerman

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

All Works

15 of 15 papers shown
1.
Zhuang, Honglei, Kai Hui, Zhen Qin, et al.. (2024). Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?. 2321–2326.
2.
Oosterhuis, Harrie, Rolf Jagerman, Zhen Qin, Xuanhui Wang, & Michael Bendersky. (2024). Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I.. arXiv (Cornell University). 2307–2317. 2 indexed citations
3.
Qin, Zhen, Rolf Jagerman, Kai Hui, et al.. (2024). Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. 1504–1518. 46 indexed citations
4.
Qin, Zhen, Honglei Zhuang, Rolf Jagerman, et al.. (2024). Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing. 410–423. 1 indexed citations
5.
Jagerman, Rolf, Zhen Qin, Le Yan, et al.. (2023). Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance. 4502–4508. 3 indexed citations
6.
Zhuang, Honglei, Zhen Qin, Rolf Jagerman, et al.. (2023). RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses. 2308–2313. 23 indexed citations
7.
Jagerman, Rolf, Zhen Qin, Xuanhui Wang, Michael Bendersky, & Marc Najork. (2022). On Optimizing Top-K Metrics for Neural Ranking Models. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2303–2307. 4 indexed citations
8.
Jagerman, Rolf, Xuanhui Wang, Honglei Zhuang, et al.. (2022). Rax: Composable Learning-to-Rank Using JAX. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3051–3060. 3 indexed citations
9.
Qin, Zhen, Honglei Zhuang, Rolf Jagerman, et al.. (2021). Bootstrapping Recommendations at Chrome Web Store. 3483–3491. 5 indexed citations
10.
Jagerman, Rolf, Weize Kong, Rama Kumar Pasumarthi, et al.. (2021). Improving Cloud Storage Search with User Activity. 508–516.
11.
Lucchese, Claudio, Franco Maria Nardini, Rama Kumar Pasumarthi, et al.. (2019). Learning to Rank in Theory and Practice. UvA-DARE (University of Amsterdam). 1419–1420. 6 indexed citations
12.
Jagerman, Rolf, Ilya Markov, & Maarten de Rijke. (2019). When People Change their Mind. UvA-DARE (University of Amsterdam). 447–455. 28 indexed citations
13.
Jagerman, Rolf, Krisztian Balog, & Maarten de Rijke. (2018). OpenSearch. Journal of Data and Information Quality. 10(3). 1–15. 12 indexed citations
14.
Jagerman, Rolf, Julia Kiseleva, & Maarten de Rijke. (2017). Modeling Label Ambiguity for List-Wise Neural Learning to Rank. Data Archiving and Networked Services (DANS). 1 indexed citations
15.
Jagerman, Rolf, Carsten Eickhoff, & Maarten de Rijke. (2017). Computing Web-scale Topic Models using an Asynchronous Parameter Server. arXiv (Cornell University). 1337–1340. 8 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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