Navid Rekabsaz

697 total citations
30 papers, 280 citations indexed

About

Navid Rekabsaz is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Navid Rekabsaz has authored 30 papers receiving a total of 280 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 10 papers in Information Systems and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Navid Rekabsaz's work include Topic Modeling (15 papers), Natural Language Processing Techniques (9 papers) and Recommender Systems and Techniques (6 papers). Navid Rekabsaz is often cited by papers focused on Topic Modeling (15 papers), Natural Language Processing Techniques (9 papers) and Recommender Systems and Techniques (6 papers). Navid Rekabsaz collaborates with scholars based in Austria, United States and Italy. Navid Rekabsaz's co-authors include Markus Schedl, Emilia Parada‐Cabaleiro, Alessandro B. Melchiorre, Allan Hanbury, Mihai Lupu, Carsten Eickhoff, George Zerveas, João Palotti, Daniel J. Cohen and Hamed Zamani and has published in prestigious journals such as Information Processing & Management, Language Resources and Evaluation and Frontiers in Big Data.

In The Last Decade

Navid Rekabsaz

29 papers receiving 273 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Navid Rekabsaz Austria 9 173 115 62 37 34 30 280
Shuyuan Xu United States 13 295 1.7× 261 2.3× 49 0.8× 24 0.6× 14 0.4× 33 431
Noemi Mauro Italy 9 135 0.8× 177 1.5× 51 0.8× 56 1.5× 30 0.9× 38 290
Wanjun Zhong China 11 411 2.4× 155 1.3× 65 1.0× 156 4.2× 23 0.7× 36 543
Yaakov HaCohen‐Kerner Israel 10 305 1.8× 105 0.9× 20 0.3× 36 1.0× 12 0.4× 42 379
Vítor Mangaravite Brazil 5 382 2.2× 130 1.1× 38 0.6× 29 0.8× 26 0.8× 10 458
Allison J. B. Chaney United States 7 105 0.6× 137 1.2× 37 0.6× 41 1.1× 10 0.3× 11 239
Manish Bhide India 7 121 0.7× 83 0.7× 19 0.3× 20 0.5× 25 0.7× 18 290
Kanthashree Mysore Sathyendra United States 7 277 1.6× 106 0.9× 85 1.4× 216 5.8× 63 1.9× 15 429
Oddur Kjartansson United States 10 277 1.6× 45 0.4× 59 1.0× 22 0.6× 117 3.4× 14 416
Pigi Kouki United States 8 199 1.2× 164 1.4× 51 0.8× 19 0.5× 17 0.5× 9 287

Countries citing papers authored by Navid Rekabsaz

Since Specialization
Citations

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

Fields of papers citing papers by Navid Rekabsaz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Navid Rekabsaz

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

All Works

20 of 20 papers shown
1.
Zerveas, George, Navid Rekabsaz, & Carsten Eickhoff. (2023). Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors. 10779–10803. 1 indexed citations
2.
Schedl, Markus, et al.. (2023). Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks. 6192–6214. 6 indexed citations
3.
Deldjoo, Yashar, et al.. (2023). Computational Versus Perceived Popularity Miscalibration in Recommender Systems. University Library Linz repository (Johannes Kepler Universitat Linz). 1889–1893. 3 indexed citations
4.
Rekabsaz, Navid, et al.. (2023). Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives. Frontiers in Big Data. 6. 1245198–1245198. 6 indexed citations
5.
Rocca, Roberta, et al.. (2023). Natural language processing for humanitarian action: Opportunities, challenges, and the path toward humanitarian NLP. Frontiers in Big Data. 6. 1082787–1082787. 11 indexed citations
6.
Zerveas, George, et al.. (2023). Parameter-efficient Modularised Bias Mitigation via AdapterFusion. 4 indexed citations
7.
Rekabsaz, Navid, et al.. (2023). Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification. 6219–6227. 4 indexed citations
8.
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10.
Zerveas, George, Navid Rekabsaz, Daniel A. Cohen, & Carsten Eickhoff. (2022). CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking. 10626–10644. 6 indexed citations
11.
Melchiorre, Alessandro B., et al.. (2022). ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations. University Library Linz repository (Johannes Kepler Universitat Linz). 246–256. 15 indexed citations
12.
Zerveas, George, Navid Rekabsaz, Daniel J. Cohen, & Carsten Eickhoff. (2022). Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2532–2538. 10 indexed citations
13.
Rekabsaz, Navid, et al.. (2022). Unlearning Protected User Attributes in Recommendations with Adversarial Training. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2142–2147. 24 indexed citations
14.
Rekabsaz, Navid, et al.. (2021). WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. arXiv (Cornell University). 15 indexed citations
15.
Rekabsaz, Navid, Mihai Lupu, Allan Hanbury, & Hamed Zamani. (2017). Word Embedding Causes Topic Shifting; Exploit Global Context!. 1105–1108. 10 indexed citations
16.
Rekabsaz, Navid, et al.. (2016). Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation. Language Resources and Evaluation. 4176–4179. 1 indexed citations
17.
Rekabsaz, Navid, Mihai Lupu, Allan Hanbury, & Guido Zuccon. (2016). Generalizing Translation Models in the Probabilistic Relevance Framework. 711–720. 8 indexed citations
18.
Palotti, João, et al.. (2015). TUW @ MediaEval 2015 Retrieving Diverse Social Images Task. MediaEval. 4 indexed citations
19.
Rekabsaz, Navid, et al.. (2015). Open Government Data as a Service (GoDaaS): Big Data Platform for Mobile App Developers. 398–403. 4 indexed citations
20.
Palotti, João, Navid Rekabsaz, Linda Anderson, & Allan Hanbury. (2014). TUW @ TREC Clinical Decision Support Track. Text REtrieval Conference. 7 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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