A Real-World Framework for Translator as Expert Retrieval. - Navid Rekabsaz; Mihai Lupu (CLEF, 2014)

CEA LIST's Participation at the MediaEval 2014 Retrieving Diverse Social Images Task. - Alexandru-Lucian Gînsca; Adrian Popescu 001; Navid Rekabsaz (MediaEval, 2014)

TUW @ Retrieving Diverse Social Images Task 2014. - João R. M. Palotti; Navid Rekabsaz; Mihai Lupu; Allan Hanbury (MediaEval, 2014)

TUW @ TREC Clinical Decision Support Track. - João R. M. Palotti; Navid Rekabsaz; Linda Andersson; Allan Hanbury (TREC, 2014)

On the use of statistical semantics for metadata-based social image retrieval. - Navid Rekabsaz; Ralf Bierig; Bogdan Ionescu; Allan Hanbury; Mihai Lupu (CBMI, 2015)

Open Government Data as a Service (GoDaaS): Big Data Platform for Mobile App Developers. - Soheil Qanbari; Navid Rekabsaz; Schahram Dustdar (FiCloud, 2015)

TUW @ MediaEval 2015 Retrieving Diverse Social Images Task. - Serwah Sabetghadam; João R. M. Palotti; Navid Rekabsaz; Mihai Lupu; Allan Hanbury (MediaEval, 2015)

Generalizing Translation Models in the Probabilistic Relevance Framework. - Navid Rekabsaz; Mihai Lupu; Allan Hanbury; Guido Zuccon (CIKM, 2016)

Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation. - Navid Rekabsaz; Serwah Sabetghadam; Mihai Lupu; Linda Andersson; Allan Hanbury (LREC, 2016)

Enhancing Information Retrieval with Adapted Word Embedding. - Navid Rekabsaz (SIGIR, 2016)

Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity. - Navid Rekabsaz; Mihai Lupu; Allan Hanbury (CoRR, 2016)

Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models. - Navid Rekabsaz; Mihai Lupu; Artem Baklanov; Alexander Dür; Linda Andersson; Allan Hanbury (ACL (1), 2017)

Exploring Understandability Features to Personalize Consumer Health Search. TUW at CLEF 2017 eHealth. - João R. M. Palotti; Navid Rekabsaz (CLEF (Working Notes), 2017)

Exploration of a Threshold for Similarity Based on Uncertainty in Word Embedding. - Navid Rekabsaz; Mihai Lupu; Allan Hanbury (ECIR, 2017)

Word Embedding Causes Topic Shifting; Exploit Global Context! - Navid Rekabsaz; Mihai Lupu; Allan Hanbury; Hamed Zamani (SIGIR, 2017)

Toward Incorporation of Relevant Documents in word2vec. - Navid Rekabsaz; Bhaskar Mitra 001; Mihai Lupu; Allan Hanbury (CoRR, 2017)

Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian. - Navid Rekabsaz; Mihai Lupu; Allan Hanbury; Andrés Duque (CoRR, 2017)

Toward Optimized Multimodal Concept Indexing. - Navid Rekabsaz; Ralf Bierig; Mihai Lupu; Allan Hanbury (Trans. Comput. Collect. Intell., 2017)

An Unbiased Approach to Quantification of Gender Inclination using Interpretable Word Representations. - Navid Rekabsaz; Allan Hanbury (CoRR, 2018)

Efficient Answer-Annotation for Frequent Questions. - Markus Zlabinger; Navid Rekabsaz; Stefan Zlabinger; Allan Hanbury (CLEF, 2019)

Enriching Word Embeddings for Patent Retrieval with Global Context. - Sebastian Hofstätter; Navid Rekabsaz; Mihai Lupu; Carsten Eickhoff; Allan Hanbury (ECIR (1), 2019)

On the Effect of Low-Frequency Terms on Neural-IR Models. - Sebastian Hofstätter; Navid Rekabsaz; Carsten Eickhoff; Allan Hanbury (SIGIR, 2019)

Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains. - Navid Rekabsaz; Nikolaos Pappas 002; James Henderson 001; Banriskhem K. Khonglah; Srikanth R. Madikeri (CoRR, 2019)

DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations. - Markus Zlabinger; Sebastian Hofstätter; Navid Rekabsaz; Allan Hanbury (ECIR (2), 2020)

Do Neural Ranking Models Intensify Gender Bias? - Navid Rekabsaz; Markus Schedl (SIGIR, 2020)

MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization. - Jenny Paola Yela-Bello; Ewan Oglethorpe; Navid Rekabsaz (EACL, 2021)

A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models. - Oleg Lesota; Navid Rekabsaz; Daniel Cohen; Klaus Antonius Grasserbauer; Carsten Eickhoff; Markus Schedl (ICTIR, 2021)

Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence. - Navid Rekabsaz; Robert West 001; James Henderson 001; Allan Hanbury (ICWSM, 2021)

Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? - Oleg Lesota; Alessandro B. Melchiorre; Navid Rekabsaz; Stefan Brandl; Dominik Kowald; Elisabeth Lex; Markus Schedl (RecSys, 2021)

Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. - Daniel Cohen; Bhaskar Mitra 001; Oleg Lesota; Navid Rekabsaz; Carsten Eickhoff (SIGIR, 2021)

Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation of BERT Rankers. - Navid Rekabsaz; Simone Kopeinik; Markus Schedl (SIGIR, 2021)

TripClick: The Log Files of a Large Health Web Search Engine. - Navid Rekabsaz; Oleg Lesota; Markus Schedl; Jon Brassey; Carsten Eickhoff (SIGIR, 2021)

CODER: An efficient framework for improving retrieval through COntextualized Document Embedding Reranking. - George Zerveas; Navid Rekabsaz; Daniel Cohen; Carsten Eickhoff (CoRR, 2021)

Investigating gender fairness of recommendation algorithms in the music domain. - Alessandro B. Melchiorre; Navid Rekabsaz; Emilia Parada-Cabaleiro; Stefan Brandl; Oleg Lesota; Markus Schedl (Inf. Process. Manag., 2021)

Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? - Klara Krieg; Emilia Parada-Cabaleiro; Markus Schedl; Navid Rekabsaz (BIAS, 2022)

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 (CHIIR, 2022)

HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response. - Selim Fekih; Nicolò Tamagnone; Benjamin Minixhofer; Ranjan Shrestha; Ximena Contla; Ewan Oglethorpe; Navid Rekabsaz (EMNLP (Findings), 2022)

CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking. - George Zerveas; Navid Rekabsaz; Daniel Cohen; Carsten Eickhoff (EMNLP, 2022)

Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms. - Oleg Lesota; Emilia Parada-Cabaleiro; Stefan Brandl; Elisabeth Lex; Navid Rekabsaz; Markus Schedl (ISMIR, 2022)

WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. - Benjamin Minixhofer; Fabian Paischer; Navid Rekabsaz (NAACL-HLT, 2022)

Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization. - Oleg Lesota; Stefan Brandl; Matthias Wenzel; Alessandro B. Melchiorre; Elisabeth Lex; Navid Rekabsaz; Markus Schedl (MORS@RecSys, 2022)

ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations. - Alessandro B. Melchiorre; Navid Rekabsaz; Christian Ganhör; Markus Schedl (RecSys, 2022)

Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences. - Daniel Cohen; Kevin Du; Bhaskar Mitra 001; Laura Mercurio; Navid Rekabsaz; Carsten Eickhoff (SIGIR, 2022)

Unlearning Protected User Attributes in Recommendations with Adversarial Training. - Christian Ganhör; David Penz; Navid Rekabsaz; Oleg Lesota; Markus Schedl (SIGIR, 2022)

Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization. - George Zerveas; Navid Rekabsaz; Daniel Cohen; Carsten Eickhoff (SIGIR, 2022)

Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research. - Markus Schedl; Navid Rekabsaz; Elisabeth Lex; Tessa Grosz; Elisabeth Greif (UMAP (Adjunct Publication), 2022)

Parameter Efficient Diff Pruning for Bias Mitigation. - Lukas Hauzenberger; Navid Rekabsaz (CoRR, 2022)

HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response. - Selim Fekih; Nicolò Tamagnone; Benjamin Minixhofer; Ranjan Shrestha; Ximena Contla; Ewan Oglethorpe; Navid Rekabsaz (CoRR, 2022)

Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks. - Lukas Hauzenberger; Shahed Masoudian; Deepak Kumar; Markus Schedl; Navid Rekabsaz (ACL (Findings), 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; Navid Rekabsaz (CHI, 2023)

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 (CHIIR, 2023)

Parameter-efficient Modularised Bias Mitigation via AdapterFusion. - Deepak Kumar; Oleg Lesota; George Zerveas; Daniel Cohen; Carsten Eickhoff; Markus Schedl; Navid Rekabsaz (EACL, 2023)

Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors. - George Zerveas; Navid Rekabsaz; Carsten Eickhoff (EMNLP, 2023)

Domain Information Control at Inference Time for Acoustic Scene Classification. - Shahed Masoudian; Khaled Koutini; Markus Schedl; Gerhard Widmer; Navid Rekabsaz (EUSIPCO, 2023)

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 (HR@RecSys, 2023)

Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification. - Nicolò Tamagnone; Selim Fekih; Ximena Contla; Nayid Orozco; Navid Rekabsaz (IJCAI, 2023)

Computational Versus Perceived Popularity Miscalibration in Recommender Systems. - Oleg Lesota; Gustavo Escobedo; Yashar Deldjoo; Bruce Ferwerda; Simone Kopeinik; Elisabeth Lex; Navid Rekabsaz; Markus Schedl (SIGIR, 2023)

Leveraging Vision-Language Models for Granular Market Change Prediction. - Christopher Wimmer; Navid Rekabsaz (CoRR, 2023)

Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors. - George Zerveas; Navid Rekabsaz; Carsten Eickhoff (CoRR, 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 (Frontiers Big Data, 2023)

ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale. - Markus Frohmann; Carolin Holtermann; Shahed Masoudian; Anne Lauscher; Navid Rekabsaz (ACL (Findings), 2024)

What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition. - Carolin Holtermann; Markus Frohmann; Navid Rekabsaz; Anne Lauscher (EACL (Findings), 2024)

Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters. - Shahed Masoudian; Cornelia Volaucnik; Markus Schedl; Navid Rekabsaz (EACL (1), 2024)

Measuring Bias in Search Results Through Retrieval List Comparison. - Linda Ratz; Markus Schedl; Simone Kopeinik; Navid Rekabsaz (ECIR (5), 2024)

Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization. - Shahed Masoudian; Markus Frohmann; Navid Rekabsaz; Markus Schedl (EMNLP, 2024)

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 Big Data, 2024)

Batched Self-Consistency Improves LLM Relevance Assessment and Ranking. - Anton Korikov; Pan Du; Scott Sanner; Navid Rekabsaz (CoRR, 2025)