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2020 – today
- 2024
[j4]Deepak Kumar
, Tessa Grosz, Navid Rekabsaz, Elisabeth Greif, Markus Schedl:
Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives. Frontiers Big Data 6 (2024)- [c52]Markus Frohmann, Carolin Holtermann, Shahed Masoudian, Anne Lauscher, Navid Rekabsaz:
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale. ACL (Findings) 2024: 11743-11776 - [c51]Carolin Holtermann, Markus Frohmann, Navid Rekabsaz, Anne Lauscher:
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition. EACL (Findings) 2024: 1138-1157 - [c50]Shahed Masoudian, Cornelia Volaucnik, Markus Schedl, Navid Rekabsaz:
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters. EACL (1) 2024: 2434-2453 - [c49]Linda Ratz, Markus Schedl, Simone Kopeinik, Navid Rekabsaz:
Measuring Bias in Search Results Through Retrieval List Comparison. ECIR (5) 2024: 20-34 - [i30]Carolin Holtermann, Markus Frohmann, Navid Rekabsaz, Anne Lauscher:
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition. CoRR abs/2401.12756 (2024) - [i29]Shahed Masoudian, Cornelia Volaucnik, Markus Schedl, Navid Rekabsaz:
Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters. CoRR abs/2401.16457 (2024) - [i28]Shahed Masoudian, Markus Frohmann, Navid Rekabsaz, Markus Schedl:
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization. CoRR abs/2409.19541 (2024) - 2023
- [j3]Roberta Rocca, Nicolò Tamagnone, Selim Fekih, Ximena Contla, Navid Rekabsaz:
Natural language processing for humanitarian action: Opportunities, challenges, and the path toward humanitarian NLP. Frontiers Big Data 6 (2023) - [c48]Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekabsaz:
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks. ACL (Findings) 2023: 6192-6214 - [c47]Simone Kopeinik, Martina Mara, Linda Ratz, Klara Krieg, Markus Schedl, Navid Rekabsaz:
Show me a "Male Nurse"! How Gender Bias is Reflected in the Query Formulation of Search Engine Users. CHI 2023: 137:1-137:15 - [c46]Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz:
Grep-BiasIR: A Dataset for Investigating Gender Representation Bias in Information Retrieval Results. CHIIR 2023: 444-448 - [c45]Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz:
Parameter-efficient Modularised Bias Mitigation via AdapterFusion. EACL 2023: 2730-2743 - [c44]George Zerveas, Navid Rekabsaz, Carsten Eickhoff:
Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors. EMNLP 2023: 10779-10803 - [c43]Shahed Masoudian, Khaled Koutini, Markus Schedl, Gerhard Widmer, Navid Rekabsaz:
Domain Information Control at Inference Time for Acoustic Scene Classification. EUSIPCO 2023: 181-185 - [c42]Deepak Kumar, Tessa Grosz, Elisabeth Greif, Navid Rekabsaz, Markus Schedl:
Identifying Words in Job Advertisements Responsible for Gender Bias in Candidate Ranking Systems via Counterfactual Learning. HR@RecSys 2023 - [c41]Nicolò Tamagnone, Selim Fekih, Ximena Contla, Nayid Orozco, Navid Rekabsaz:
Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification. IJCAI 2023: 6219-6227 - [c40]Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, Markus Schedl:
Computational Versus Perceived Popularity Miscalibration in Recommender Systems. SIGIR 2023: 1889-1893 - [i27]Christopher Wimmer, Navid Rekabsaz:
Leveraging Vision-Language Models for Granular Market Change Prediction. CoRR abs/2301.10166 (2023) - [i26]Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz:
Parameter-efficient Modularised Bias Mitigation via AdapterFusion. CoRR abs/2302.06321 (2023) - [i25]George Zerveas, Navid Rekabsaz, Carsten Eickhoff:
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors. CoRR abs/2305.15720 (2023) - [i24]Nicolò Tamagnone, Selim Fekih, Ximena Contla, Nayid Orozco, Navid Rekabsaz:
Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification. CoRR abs/2305.16756 (2023) - [i23]Shahed Masoudian, Khaled Koutini, Markus Schedl, Gerhard Widmer, Navid Rekabsaz:
Domain Information Control at Inference Time for Acoustic Scene Classification. CoRR abs/2306.08010 (2023) - [i22]Markus Frohmann, Carolin Holtermann, Shahed Masoudian, Anne Lauscher, Navid Rekabsaz:
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale. CoRR abs/2310.01217 (2023) - 2022
- [c39]Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz:
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? BIAS 2022: 104-116 - [c38]Markus Schedl, Stefan Brandl, Oleg Lesota, Emilia Parada-Cabaleiro, David Penz, Navid Rekabsaz:
LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis. CHIIR 2022: 337-341 - [c37]Selim Fekih, Nicolò Tamagnone, Benjamin Minixhofer, Ranjan Shrestha, Ximena Contla, Ewan Oglethorpe, Navid Rekabsaz:
HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response. EMNLP (Findings) 2022: 4379-4389 - [c36]George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff:
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking. EMNLP 2022: 10626-10644 - [c35]Oleg Lesota, Emilia Parada-Cabaleiro, Stefan Brandl, Elisabeth Lex, Navid Rekabsaz, Markus Schedl:
Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms. ISMIR 2022: 291-297 - [c34]Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz:
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. NAACL-HLT 2022: 3992-4006 - [c33]Oleg Lesota, Stefan Brandl, Matthias Wenzel, Alessandro B. Melchiorre, Elisabeth Lex, Navid Rekabsaz, Markus Schedl:
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization. MORS@RecSys 2022 - [c32]Alessandro B. Melchiorre, Navid Rekabsaz, Christian Ganhör, Markus Schedl:
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations. RecSys 2022: 246-256 - [c31]Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl:
Unlearning Protected User Attributes in Recommendations with Adversarial Training. SIGIR 2022: 2142-2147 - [c30]George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff:
Mitigating Bias in Search Results Through Contextual Document Reranking and Neutrality Regularization. SIGIR 2022: 2532-2538 - [c29]Daniel Cohen, Kevin Du, Bhaskar Mitra, Laura Mercurio, Navid Rekabsaz, Carsten Eickhoff:
Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences. SIGIR 2022: 2572-2577 - [c28]Markus Schedl, Navid Rekabsaz, Elisabeth Lex, Tessa Grosz, Elisabeth Greif:
Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research. UMAP (Adjunct Publication) 2022: 90-94 - [i21]Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz:
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results. CoRR abs/2201.07754 (2022) - [i20]Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz:
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? CoRR abs/2203.01731 (2022) - [i19]Lukas Hauzenberger, Navid Rekabsaz:
Parameter Efficient Diff Pruning for Bias Mitigation. CoRR abs/2205.15171 (2022) - [i18]Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl:
Unlearning Protected User Attributes in Recommendations with Adversarial Training. CoRR abs/2206.04500 (2022) - [i17]Selim Fekih, Nicolò Tamagnone, Benjamin Minixhofer, Ranjan Shrestha, Ximena Contla, Ewan Oglethorpe, Navid Rekabsaz:
HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response. CoRR abs/2210.04573 (2022) - 2021
- [j2]Alessandro B. Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, Markus Schedl:
Investigating gender fairness of recommendation algorithms in the music domain. Inf. Process. Manag. 58(5): 102666 (2021) - [c27]Jenny Paola Yela-Bello, Ewan Oglethorpe, Navid Rekabsaz:
MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization. EACL 2021: 1713-1717 - [c26]Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl:
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models. ICTIR 2021: 185-195 - [c25]Navid Rekabsaz, Robert West, James Henderson, Allan Hanbury:
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence. ICWSM 2021: 549-560 - [c24]Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl:
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? RecSys 2021: 601-606 - [c23]Navid Rekabsaz, Simone Kopeinik, Markus Schedl:
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation of BERT Rankers. SIGIR 2021: 306-316 - [c22]Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, Carsten Eickhoff:
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. SIGIR 2021: 654-664 - [c21]Navid Rekabsaz, Oleg Lesota, Markus Schedl, Jon Brassey, Carsten Eickhoff:
TripClick: The Log Files of a Large Health Web Search Engine. SIGIR 2021: 2507-2513 - [i16]Navid Rekabsaz, Oleg Lesota, Markus Schedl, Jon Brassey, Carsten Eickhoff:
TripClick: The Log Files of a Large Health Web Search Engine. CoRR abs/2103.07901 (2021) - [i15]Navid Rekabsaz, Simone Kopeinik, Markus Schedl:
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers. CoRR abs/2104.13640 (2021) - [i14]Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, Carsten Eickhoff:
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. CoRR abs/2105.04651 (2021) - [i13]Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl:
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models. CoRR abs/2106.13618 (2021) - [i12]Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl:
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? CoRR abs/2108.06973 (2021) - [i11]Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz:
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. CoRR abs/2112.06598 (2021) - [i10]George Zerveas, Navid Rekabsaz, Daniel Cohen, Carsten Eickhoff:
CODER: An efficient framework for improving retrieval through COntextualized Document Embedding Reranking. CoRR abs/2112.08766 (2021) - 2020
- [c20]Markus Zlabinger, Sebastian Hofstätter, Navid Rekabsaz, Allan Hanbury:
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations. ECIR (2) 2020: 433-440 - [c19]Navid Rekabsaz, Markus Schedl:
Do Neural Ranking Models Intensify Gender Bias? SIGIR 2020: 2065-2068 - [i9]Markus Zlabinger, Sebastian Hofstätter, Navid Rekabsaz, Allan Hanbury:
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations. CoRR abs/2001.05357 (2020) - [i8]Navid Rekabsaz, Markus Schedl:
Do Neural Ranking Models Intensify Gender Bias? CoRR abs/2005.00372 (2020)
2010 – 2019
- 2019
- [c18]Markus Zlabinger, Navid Rekabsaz, Stefan Zlabinger, Allan Hanbury:
Efficient Answer-Annotation for Frequent Questions. CLEF 2019: 126-137 - [c17]Sebastian Hofstätter, Navid Rekabsaz, Mihai Lupu, Carsten Eickhoff, Allan Hanbury:
Enriching Word Embeddings for Patent Retrieval with Global Context. ECIR (1) 2019: 810-818 - [c16]Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff, Allan Hanbury:
On the Effect of Low-Frequency Terms on Neural-IR Models. SIGIR 2019: 1137-1140 - [i7]Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff, Allan Hanbury:
On the Effect of Low-Frequency Terms on Neural-IR Models. CoRR abs/1904.12683 (2019) - [i6]Navid Rekabsaz, Nikolaos Pappas, James Henderson, Banriskhem K. Khonglah, Srikanth R. Madikeri:
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains. CoRR abs/1906.01496 (2019) - 2018
- [i5]Navid Rekabsaz, Allan Hanbury:
An Unbiased Approach to Quantification of Gender Inclination using Interpretable Word Representations. CoRR abs/1812.10424 (2018) - 2017
- [j1]Navid Rekabsaz, Ralf Bierig, Mihai Lupu, Allan Hanbury:
Toward Optimized Multimodal Concept Indexing. Trans. Comput. Collect. Intell. 26: 144-161 (2017) - [c15]Navid Rekabsaz, Mihai Lupu, Artem Baklanov, Alexander Dür, Linda Andersson, Allan Hanbury:
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models. ACL (1) 2017: 1712-1721 - [c14]João R. M. Palotti, Navid Rekabsaz:
Exploring Understandability Features to Personalize Consumer Health Search. TUW at CLEF 2017 eHealth. CLEF (Working Notes) 2017 - [c13]Navid Rekabsaz, Mihai Lupu, Allan Hanbury:
Exploration of a Threshold for Similarity Based on Uncertainty in Word Embedding. ECIR 2017: 396-409 - [c12]Navid Rekabsaz, Mihai Lupu, Allan Hanbury, Hamed Zamani:
Word Embedding Causes Topic Shifting; Exploit Global Context! SIGIR 2017: 1105-1108 - [i4]Navid Rekabsaz, Mihai Lupu, Artem Baklanov, Allan Hanbury, Alexander Dür, Linda Andersson:
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models. CoRR abs/1702.01978 (2017) - [i3]Navid Rekabsaz, Bhaskar Mitra, Mihai Lupu, Allan Hanbury:
Toward Incorporation of Relevant Documents in word2vec. CoRR abs/1707.06598 (2017) - [i2]Navid Rekabsaz, Mihai Lupu, Allan Hanbury, Andrés Duque:
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian. CoRR abs/1711.06196 (2017) - 2016
- [c11]Navid Rekabsaz, Mihai Lupu, Allan Hanbury, Guido Zuccon:
Generalizing Translation Models in the Probabilistic Relevance Framework. CIKM 2016: 711-720 - [c10]Navid Rekabsaz, Serwah Sabetghadam, Mihai Lupu, Linda Andersson, Allan Hanbury:
Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation. LREC 2016 - [c9]Navid Rekabsaz:
Enhancing Information Retrieval with Adapted Word Embedding. SIGIR 2016: 1169 - [i1]Navid Rekabsaz, Mihai Lupu, Allan Hanbury:
Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity. CoRR abs/1606.06086 (2016) - 2015
- [c8]Navid Rekabsaz, Ralf Bierig, Bogdan Ionescu, Allan Hanbury, Mihai Lupu:
On the use of statistical semantics for metadata-based social image retrieval. CBMI 2015: 1-4 - [c7]Navid Rekabsaz, Ralf Bierig, Mihai Lupu, Allan Hanbury:
Toward Optimized Multimodal Concept Indexing. International KEYSTONE Conference 2015: 141-152 - [c6]Soheil Qanbari, Navid Rekabsaz, Schahram Dustdar:
Open Government Data as a Service (GoDaaS): Big Data Platform for Mobile App Developers. FiCloud 2015: 398-403 - [c5]Serwah Sabetghadam, João R. M. Palotti, Navid Rekabsaz, Mihai Lupu, Allan Hanbury:
TUW @ MediaEval 2015 Retrieving Diverse Social Images Task. MediaEval 2015 - 2014
- [c4]Navid Rekabsaz, Mihai Lupu:
A Real-World Framework for Translator as Expert Retrieval. CLEF 2014: 141-152 - [c3]Alexandru-Lucian Gînsca, Adrian Popescu, Navid Rekabsaz:
CEA LIST's Participation at the MediaEval 2014 Retrieving Diverse Social Images Task. MediaEval 2014 - [c2]João R. M. Palotti, Navid Rekabsaz, Mihai Lupu, Allan Hanbury:
TUW @ Retrieving Diverse Social Images Task 2014. MediaEval 2014 - [c1]João R. M. Palotti, Navid Rekabsaz, Linda Andersson, Allan Hanbury:
TUW @ TREC Clinical Decision Support Track. TREC 2014
Coauthor Index
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