Navid Rekab-saz

Navid Rekab-saz is an Senior Applied Machine Learning Scientist at Thomson Reuters AI Labs. Prior to that, he was an Assistant Professor at Johannes Kepler University Linz, and a post doctoral researcher at Idiap Research Institute. Navid is a member of ELLIS Society, and a Senior Technical Advisor at Data Friendly Space INGO.
His work contributes to the foundations of Deep Learning and Natural Language Processing with a focus on their socio-technological context. He also explores the methodological principles of harnessing these methods for positive impact, applied particularly to the humanitarian response and climate change mitigation domains.



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Publications

2024
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters
Shahed Masoudian, Cornelia Volaucnik, Markus Schedl, Navid Rekabsaz
In proceedings of the 18th Annual Meeting of the European chapter of the Association for Computational Linguistics (EACL), March 2024.
PrePrint
Measuring Bias in Search Results Through Retrieval List Comparison
Linda Ratz, Markus Schedl, Simone Kopeinik, Navid Rekabsaz
In proceedings of the 46th European Conference on Information Retrieval (ECIR), March 2024.
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
Carolin Holtermann, Markus Frohmann, Navid Rekabsaz, Anne Lauscher
In Findings of the Annual Meeting of the European chapter of the Association for Computational Linguistics (Findings of EACL), March 2024.
PrePrint
2023
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors
George Zerveas, Navid Rekabsaz, Carsten Eickhoff
In proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), December 2023.
ACL    arXiv
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks
Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekabsaz
In Findings of the Association for Computational Linguistics: ACL (Findings of ACL), 2023
ACL    arXiv    Code
Parameter-efficient Modularised Bias Mitigation via AdapterFusion
Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz
In proceedings of 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL), May 2023.
ACL    arXiv    Code
Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification
Nicolo' Tamagnone, Selim Fekih, Ximena Contla, Nayid Orozco, Navid Rekabsaz
In proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023.
IJCAI    arXiv    Code
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, Navid Rekabsaz
In proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), 2023.
ACM    Preprint    Supplementary material    Presentation video    Repository
Computational Versus Perceived Popularity Miscalibration in Recommender Systems
Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz and Markus Schedl
In proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023
ACM    Preprint
Natural Language Processing for humanitarian action: opportunities, challenges, and the path towards humanitarian NLP
Roberta Rocca, Nicolò Tamagnone, Selim Fekih, Ximena Contla, Navid Rekabsaz
Frontiers in Big Data. 2023
frontiers
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
Frontiers in Big Data. 2023
frontiers
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results
Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz
In proceedings of the 2023 ACM SIGIR Conference On Human Information Interaction And Retrieval (CHIIR), March 2023.
ACM    arXiv    Dataset
Domain Information Control at Inference Time for Acoustic Scene Classification
Shahed Masoudian, Khaled Koutini, Markus Schedl, Gerhard Widmer, Navid Rekabsaz
In proceedings of the 31st European Signal Processing Conference (EUSIPCO), 2023
Preprint
Leveraging Vision-Language Models for Granular Market Change Prediction
Christopher Wimmer, Navid Rekabsaz
In proceedings of the Workshop On Multimodal AI For Financial Forecasting at Association for the Advancement of Artificial Intelligence (Muffin@AAAI), 2023
arXiv
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
In proceedings of the Workshop On Recommender Systems for Human Resources (RecSys in HR), 2023
CEUR-WS
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale
Markus Frohmann, Carolin Holtermann, Shahed Masoudian, Anne Lauscher, Navid Rekabsaz
arXiv
2022
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking
George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff
In proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), December 2022.
ACL    arXiv
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models
Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz
In proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), July 2022.
ACL    arXiv
HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response
Selim Fekih, Nicolo' Tamagnone, Benjamin Minixhofer, Ranjan Shrestha, Ximena Contla, Ewan Oglethorpe and Navid Rekabsaz
In Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP), December 2022.
ACL    arXiv    Dataset
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl
In proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), July 2022.
ACM    arXiv
Mitigating bias in search results through set-based document reranking and neutrality regularization
George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff
In proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), July 2022.
ACM    
Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences
Daniel Cohen, Kevin Du, Bhaskar Mitra, Laura Mercurio, Navid Rekabsaz, Carsten Eickhoff
In proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), July 2022.
ACM    
Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms
Oleg Lesota, Emilia Parada-Cabaleiro, Elisabeth Lex, Navid Rekabsaz, Stefan Brandl, Markus Schedl
In proceedings of the 23rd International Society for Music Information Retrieval Conference (ISMIR), December 2022.
**Best Student Paper**  
Paper, poster, and video
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations
Alessandro B. Melchiorre, Navid Rekabsaz, Christian Ganhör, Markus Schedl
In proceedings of the 16th ACM Recommender Systems Conference (RecSys), Sep 2022.
ACM    Preprint    code
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, Navid Rekabsaz
In proceedings of ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR), 2022
ACM    dataset
Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research
Markus Schedl, Navid Rekabsaz, Elisabeth Lex, Tessa Grosz, Elisabeth Greif
In Adjunct proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '22 Adjunct), 2022
ACM    
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?
Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz
In proceedings of the European Conference on Information Retrieval - Algorithmic Bias in Search and Recommendation Workshop (BIAS@ECIR), April 2022
Springer    arXiv
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization
Oleg Lesota, Stefan Brandl, Matthias Wenzel, Alessandro Benedetto Melchiorre, Elisabeth Lex, Navid Rekabsaz, Markus Schedl
In proceedings of the 16th ACM Recommender Systems Conference - Multi-Objective Recommender Systems Workshop (MORS@RecSys), Sep 2022.
CEUR-WS
2021
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation of BERT Rankers
Navid Rekabsaz, Simone Kopeinik, Markus Schedl
In proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), July 2021.
ACM    arXiv    code & dataset    Huggingface models
TripClick: The Log Files of a Large Health Web Search Engine
Navid Rekabsaz, Oleg Lesota, Markus Schedl, Jon Brassey, Carsten Eickhoff
In proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), July 2021.
ACM    arXiv    collection
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, Carsten Eickhoff
In proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), July 2021.
ACM    arXiv
Investigating Gender Fairness of Recommendation Algorithms in the Music Domain
Melchiorre A., Rekabsaz N., Parada-Cabaleiro E., Brandl S., Lesota O., Schedl M.
Information Processing & Management (IP&M), 2021
Elsevier    dataset
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence
Navid Rekabsaz, Robert West, James Henderson, Allan Hanbury
In proceedings of the International AAAI Conference on Web and Social Media (ICWSM) 2021
AAAI    arXiv    code
MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization
Jenny Paola Yela Bello, Ewan Oglethorpe, Navid Rekabsaz
In proceedings of the 16th Conference of the European Chapter of Association for Computational Linguistics (EACL), April 2021
ACL    collection
Analyzing item popularity bias of music recommender systems: Are different genders equally affected?
Lesota O., Melchiorre A., Rekabsaz N., Brandl S., Kowald D., Lex E., Schedl M.
In proceedings of the 15th ACM Conference on Recommender Systems (RecSys), 2021
ACM
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Lesota O., Rekabsaz N., Cohen D., Grasserbauer K., Eickhoff C., Schedl M.
In proceedings of the 7th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR), July 2021
ACM    arXiv    code
2020
Do Neural Ranking Models Intensify Gender Bias?
Navid Rekabsaz, Markus Schedl
In proceedings of the ACM Conference on Research and Development in Information Retrieval (SIGIR), 2020
ACM    arXiv    code
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations
Markus Zlabinger, Sebastian Hofstätter, Navid Rekabsaz, and Allan Hanbury
In proceedings of the European Conference on Information Retrieval (ECIR), 2020
Springer    arXiv
2019
On the Effect of Low-Frequency Terms on Neural-IR Models
Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff and Allan Hanbury
In proceedings of the ACM Conference on Research and Development in Information Retrieval (SIGIR), 2019
ACM    arXiv
Enriching Word Embeddings for Patent Retrieval with Global Context
Sebastian Hofstätter, Navid Rekabsaz, Mihai Lupu, Carsten Eickhoff and Allan Hanbury
In proceedings of the European Conference on Information Retrieval (ECIR), 2019
Springer
Efficient Answer-Annotation for Frequent Questions
Markus Zlabinger, Navid Rekabsaz, Stefan Zlabinger and Allan Hanbury
In proceedings of the Conference and Labs of the Evaluation Forum (CLEF), 2019
Springer
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains
Navid Rekabsaz, Nikolaos Pappas, James Henderson, Banriskhem K. Khonglah, Srikanth Madikeri
arXiv
2018
Word Representation for Text Analysis and Search: Document Retrieval, Sentiment Analysis, and Cross Lingual Word Sense Disambiguation
Navid Rekabsaz
PhD Thesis. Doktor der Technischen Wissenschaften
Dissertation
2017
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
Navid Rekabsaz, Mihai Lupu, Artem Baklanov, Allan Hanbury, Alexander Duer, Linda Anderson
In proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2017
ACL    arXiv    collection
Word Embedding Causes Topic Shifting; Exploit Global Context!
Navid Rekabsaz, Mihai Lupu, Hamed Zamani, Allan Hanbury
In proceedings of the ACM Conference on Research and Development in Information Retrieval (SIGIR), 2017
ACM
Exploration of a Threshold for Similarity based on Uncertainty in Word Embedding
Navid Rekabsaz, Mihai Lupu, Allan Hanbury
In proceedings of the European Conference on Information Retrieval (ECIR), 2017
Springer
Toward Incorporation of Relevant Documents in word2vec
Navid Rekabsaz, Bhaskar Mitra, Mihai Lupu, Allan Hanbury
Neu-IR Workshop at the ACM Conference on Research and Development in Information Retrieval (NeuIR-SIGIR), 2017
arXiv
2016
Generalizing Translation Models in the Probabilistic Relevance Framework
Navid Rekabsaz, Mihai Lupu, Allan Hanbury, Guido Zuccon
In proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), 2016
ACM
Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation
Navid Rekabsaz, Serwah Sabetghadam, Mihai Lupu, Linda Andersson, Allan Hanbury
In proceedings of the Language Resources and Evaluation Conference (LREC) 2016
ACL
Toward Optimized Multimodal Concept Indexing
Navid Rekabsaz, Ralf Bierig, Mihai Lupu, Allan Hanbury
Journal of Transactions on Computational Collective Intelligence (TCCI) 2016
Springer
Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity
Navid Rekabsaz, Mihai Lupu, Allan Hanbury
Neu-IR Workshop at the ACM Conference on Research and Development in Information Retrieval (NeuIR-SIGIR) 2016
arXiv
2015
Toward Optimized Multimodal Concept Indexing
Navid Rekabsaz, Ralf Bierig, Mihai Lupu, Allan Hanbury
In proceedings of the 1st International KEYSTONE Conference (IKC) 2015
Springer
Open Government data as a Service (GoDaaS): Big data Platform for Mobile App Developers
Soheil Qanbari, Navid Rekabsaz, Schahram Dustdar
In proceedings of the IEEE International Conference on Open and Big data (IEEE OBD) 2015
IEEE
On the Use of Statistical Semantics for Metadata-based Social Image Retrieval
Navid Rekabsaz, Ralf Bierig, Bogdan Ionescu, Allan Hanbury, Mihai Lupu
In proceedings of the 13th International Workshop on Content-Based Multimedia Indexing (CBMI) 2015
IEEE
TUW @ MediaEval 2015 Retrieving Diverse Social Images Task
João R. M. Palotti, Serwah Sabetghadam, Navid Rekabsaz, Mihai Lupu, Allan Hanbury
In proceedings of the MediaEval Workshop 2015
CEUR-WS
2014
A Real-World Framework for Translator as Expert Retrieval
Navid Rekabsaz, Mihai Lupu
In proceedings of the of the 5th international Conference and Labs of the Evaluation Forum (CLEF) 2014
Springer
TUW @ TREC Clinical Decision Support Track
João R. M. Palotti, Navid Rekabsaz, Linda Anderson, Allan Hanbury
In proceedings of the Twenty-Third Text REtrieval Conference (TREC) 2014
TREC
TUW @ Retrieving Diverse Social Images Task
João R. M. Palotti, Navid Rekabsaz, Mihai Lupu, Allan Hanbury
In proceedings of the MediaEval Workshop, 2014
CEUR-WS
CEA LIST’s Participation at the MediaEval 2014 Retrieving Diverse Social Images Task
Alexandru Lucian Gînscă, Adrian Popescu, Navid Rekabsaz
In proceedings of the MediaEval Workshop, 2014
CEUR-WS