Fairness in Information Retrieval
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Summary
The main goal of this Ph.D. is to increase the stability and reusability of existing test collections, when to be evaluated are systems in terms of precision, recall, and accessibility.
- Type
- article
- Published
- 2016-07-07
- Cited by
- 20
- References
- 5
- OpenAlex
- https://openalex.org/W2469819362
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:18752886
Keywords
Pooling, Computer science, Information retrieval, Relevance (law), Data collection
References
Cited by
- The Solitude of Relevant Documents in the Pool
- Fixed budget pooling strategies based on fusion methods
- Visual Pool: A Tool to Visualize and Interact with the Pooling Method
- On Biases in Information Retrieval Models and Evaluation
- Exploring Fairness and Accuracy of Retrieval Models
- Fixed-Cost Pooling Strategies
- Learning to Re-Rank with Contextualized Stopwords
- Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
- Allowing for The Grounded Use of Temporal Difference Learning in Large Ranking Models via Substate Updates
- A Comprehensive Evaluation of Biomedical Entity-centric Search
- A Taxation Perspective for Fair Re-ranking
- ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style Queries
- Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics
- Topic-Specific Classifiers are Better Relevance Judges than Prompted LLMs
- Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
- BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
- TU Wien at TREC DL and Podcast 2021: Simple Compression for Dense Retrieval
- Fixed-Cost Pooling Strategies Based on IR Evaluation Measures
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