Navid Rekabsaz

About Navid Rekabsaz

Navid Rekabsaz, With an exceptional h-index of 17 and a recent h-index of 17 (since 2020), a distinguished researcher at Johannes Kepler Universität Linz, specializes in the field of Deep Learning, Natural Language Processing, Information Retrieval.

His recent articles reflect a diverse array of research interests and contributions to the field:

Measuring Bias in Search Results Through Retrieval List Comparison

What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition

Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning

Natural language processing for humanitarian action: Opportunities, challenges, and the path toward humanitarian NLP

Domain Information Control at Inference Time for Acoustic Scene Classification

Grep-biasir: A dataset for investigating gender representation bias in information retrieval results

Computational Versus Perceived Popularity Miscalibration in Recommender Systems

Parameter-efficient modularised bias mitigation via AdapterFusion

Navid Rekabsaz Information

University

Position

Assistant Professor (JKU)

Citations(all)

908

Citations(since 2020)

805

Cited By

234

hIndex(all)

17

hIndex(since 2020)

17

i10Index(all)

22

i10Index(since 2020)

22

Email

University Profile Page

Google Scholar

Navid Rekabsaz Skills & Research Interests

Deep Learning

Natural Language Processing

Information Retrieval

Top articles of Navid Rekabsaz

Title

Journal

Author(s)

Publication Date

Measuring Bias in Search Results Through Retrieval List Comparison

Linda Ratz

Markus Schedl

Simone Kopeinik

Navid Rekabsaz

2024/3/23

What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition

arXiv preprint arXiv:2401.12756

Carolin Holtermann

Markus Frohmann

Navid Rekabsaz

Anne Lauscher

2024/1/23

Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning

Deepak Kumar

Tessa Grosz

Elisabeth Greif

Navid Rekabsaz

Markus Schedl

2023

Natural language processing for humanitarian action: Opportunities, challenges, and the path toward humanitarian NLP

Roberta Rocca

Nicolò Tamagnone

Selim Fekih

Ximena Contla

Navid Rekabsaz

2023/3/24

Domain Information Control at Inference Time for Acoustic Scene Classification

Shahed Masoudian

Khaled Koutini

Markus Schedl

Gerhard Widmer

Navid Rekabsaz

2023/9/4

Grep-biasir: A dataset for investigating gender representation bias in information retrieval results

Klara Krieg

Emilia Parada-Cabaleiro

Gertraud Medicus

Oleg Lesota

Markus Schedl

...

2023/3/19

Computational Versus Perceived Popularity Miscalibration in Recommender Systems

Oleg Lesota

Gustavo Escobedo

Yashar Deldjoo

Bruce Ferwerda

Simone Kopeinik

...

2023/7/19

Parameter-efficient modularised bias mitigation via AdapterFusion

arXiv preprint arXiv:2302.06321

Deepak Kumar

Oleg Lesota

George Zerveas

Daniel Cohen

Carsten Eickhoff

...

2023/2/13

Leveraging domain knowledge for inclusive and bias-aware humanitarian response entry classification

arXiv preprint arXiv:2305.16756

Nicolò Tamagnone

Selim Fekih

Ximena Contla

Nayid Orozco

Navid Rekabsaz

2023/5/26

Leveraging vision-language models for granular market change prediction

arXiv preprint arXiv:2301.10166

Christopher Wimmer

Navid Rekabsaz

2023/1/17

Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors

arXiv preprint arXiv:2305.15720

George Zerveas

Navid Rekabsaz

Carsten Eickhoff

2023/5/25

Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors

George Zerveas

Navid Rekabsaz

Carsten Eickhoff

2023/12

Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives

Deepak Kumar

Tessa Grosz

Navid Rekabsaz

Elisabeth Greif

Markus Schedl

2023

Show me a" Male Nurse"! How Gender Bias is Reflected in the Query Formulation of Search Engine Users

Simone Kopeinik

Martina Mara

Linda Ratz

Klara Krieg

Markus Schedl

...

2023/4/19

ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale

arXiv preprint arXiv:2310.01217

Markus Frohmann

Carolin Holtermann

Shahed Masoudian

Anne Lauscher

Navid Rekabsaz

2023/10/2

LFM-2b: A dataset of enriched music listening events for recommender systems research and fairness analysis

Markus Schedl

Stefan Brandl

Oleg Lesota

Emilia Parada-Cabaleiro

David Penz

...

2022/3/14

Mitigating bias in search results through contextual document reranking and neutrality regularization

George Zerveas

Navid Rekabsaz

Daniel Cohen

Carsten Eickhoff

2022/7/6

Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization.

Oleg Lesota

Stefan Brandl

Matthias Wenzel

Alessandro B Melchiorre

Elisabeth Lex

...

2022

Inconsistent ranking assumptions in medical search and their downstream consequences

Daniel Cohen

Kevin Du

Bhaskar Mitra

Laura Mercurio

Navid Rekabsaz

...

2022/7/6

Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms.

Oleg Lesota

Emilia Parada-Cabaleiro

Stefan Brandl

Elisabeth Lex

Navid Rekabsaz

...

2022/12/4

See List of Professors in Navid Rekabsaz University(Johannes Kepler Universität Linz)

Co-Authors

academic-engine