| 2025 | EMNLP | Batched Self-Consistency Improves LLM Relevance Assessment and Ranking. | Anton Korikov, Pan Du, Scott Sanner, Navid Rekabsaz |
| 2024 | ACL | ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale. | Markus Frohmann, Carolin Holtermann, Shahed Masoudian, Anne Lauscher, Navid Rekabsaz |
| 2024 | EACL | What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition. | Carolin Holtermann, Markus Frohmann, Navid Rekabsaz, Anne Lauscher |
| 2024 | EACL | Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters. | Shahed Masoudian, Cornelia Volaucnik, Markus Schedl, Navid Rekabsaz |
| 2024 | ECIR | Measuring Bias in Search Results Through Retrieval List Comparison. | Linda Ratz, Markus Schedl, Simone Kopeinik, Navid Rekabsaz |
| 2024 | EMNLP | Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization. | Shahed Masoudian, Markus Frohmann, Navid Rekabsaz, Markus Schedl |
| 2023 | ACL | Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks. | Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekabsaz |
| 2023 | CHI | 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 |
| 2023 | CHIIR | 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 |
| 2023 | EACL | Parameter-efficient Modularised Bias Mitigation via AdapterFusion. | Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz |
| 2023 | EMNLP | Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors. | George Zerveas, Navid Rekabsaz, Carsten Eickhoff |
| 2023 | IJCAI | Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification. | Nicol Tamagnone, Selim Fekih, Ximena Contla, Nayid Orozco, Navid Rekabsaz |
| 2023 | SIGIR | Computational Versus Perceived Popularity Miscalibration in Recommender Systems. | Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, Markus Schedl |
| 2022 | CHIIR | 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 |
| 2022 | EMNLP | HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response. | Selim Fekih, Nicol Tamagnone, Benjamin Minixhofer, Ranjan Shrestha, Ximena Contla, Ewan Oglethorpe, Navid Rekabsaz |
| 2022 | EMNLP | CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking. | George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff |
| 2022 | NAACL | WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. | Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz |
| 2022 | RecSys | 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 |
| 2022 | RecSys | ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations. | Alessandro B. Melchiorre, Navid Rekabsaz, Christian Ganhr, Markus Schedl |
| 2022 | SIGIR | Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences. | Daniel Cohen, Kevin Du, Bhaskar Mitra, Laura Mercurio, Navid Rekabsaz, Carsten Eickhoff |
| 2022 | SIGIR | Unlearning Protected User Attributes in Recommendations with Adversarial Training. | Christian Ganhr, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl |
| 2022 | SIGIR | Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization. | George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff |
| 2021 | EACL | MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization. | Jenny Paola Yela-Bello, Ewan Oglethorpe, Navid Rekabsaz |
| 2021 | ICTIR | A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models. | Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl |
| 2021 | ICWSM | Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence. | Navid Rekabsaz, Robert West, James Henderson, Allan Hanbury |
| 2021 | RecSys | 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 |
| 2021 | SIGIR | 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 |
| 2021 | SIGIR | Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation of BERT Rankers. | Navid Rekabsaz, Simone Kopeinik, Markus Schedl |
| 2021 | SIGIR | TripClick: The Log Files of a Large Health Web Search Engine. | Navid Rekabsaz, Oleg Lesota, Markus Schedl, Jon Brassey, Carsten Eickhoff |
| 2020 | ECIR | DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations. | Markus Zlabinger, Sebastian Hofsttter, Navid Rekabsaz, Allan Hanbury |
| 2020 | SIGIR | Do Neural Ranking Models Intensify Gender Bias? | Navid Rekabsaz, Markus Schedl |
| 2019 | ECIR | Enriching Word Embeddings for Patent Retrieval with Global Context. | Sebastian Hofsttter, Navid Rekabsaz, Mihai Lupu, Carsten Eickhoff, Allan Hanbury |
| 2019 | SIGIR | On the Effect of Low-Frequency Terms on Neural-IR Models. | Sebastian Hofsttter, Navid Rekabsaz, Carsten Eickhoff, Allan Hanbury |
| 2017 | ACL | Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models. | Navid Rekabsaz, Mihai Lupu, Artem Baklanov, Alexander Dr, Linda Andersson, Allan Hanbury |
| 2017 | ECIR | Exploration of a Threshold for Similarity Based on Uncertainty in Word Embedding. | Navid Rekabsaz, Mihai Lupu, Allan Hanbury |
| 2017 | SIGIR | Word Embedding Causes Topic Shifting; Exploit Global Context! | Navid Rekabsaz, Mihai Lupu, Allan Hanbury, Hamed Zamani |
| 2016 | CIKM | Generalizing Translation Models in the Probabilistic Relevance Framework. | Navid Rekabsaz, Mihai Lupu, Allan Hanbury, Guido Zuccon |
| 2016 | LREC | Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation. | Navid Rekabsaz, Serwah Sabetghadam, Mihai Lupu, Linda Andersson, Allan Hanbury |
| 2016 | SIGIR | Enhancing Information Retrieval with Adapted Word Embedding. | Navid Rekabsaz |
| 2015 | CBMI | On the use of statistical semantics for metadata-based social image retrieval. | Navid Rekabsaz, Ralf Bierig, Bogdan Ionescu, Allan Hanbury, Mihai Lupu |