Active query selection for learning rankers
Abstract
Methods that reduce the amount of labeled data needed for training have focused more on selecting which documents to label than on which queries should be labeled. One exception to this (Long et al. 2010) uses expected loss optimization (ELO) to estimate which queries should be selected but is limited to rankers that predict absolute graded relevance. In this work, we demonstrate how to easily adapt ELO to work with any ranker and show that estimating expected loss in DCG is more robust than NDCG even when the final performance measure is NDCG.
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The sampling approach biased by the selection of baseline models leads to results that are more interesting (harder for many models) than those obtained by using sampling methods directly tied to the properties of a particular model.
“While this approach is clearly biased by the selection of baseline models, in our experience, it leads to results that are more interesting (i.e., harder for many models) than those obtained by using sampling methods directly tied to the properties of a particular model (such as those obtained by active learning or adversarial approaches used to improve model training such as [3, 15]).”
From Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search (2022) · cites this paper as [3]
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Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
Reddy, C. K., Màrquez, L., Valero, F., Rao, N., Zaragoza, H., Bandyopadhyay, S., Biswas, A., Xing, A., & Subbian, K. (2022) · arXiv · doi:10.48550/arxiv.2206.06588 · Cited by 17 · see citing works
- Authors
- Bilgic, M., & Bennett, P. N.
- Affiliations
- Illinois Institute of Technology (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2012-08-12
- Pages
- 1033-1034
- OpenAlex
- W2037080275
- Cited by
- 14
- References
- 0
- Citations by year
- 2024: 1 · 2022: 2 · 2021: 2 · 2020: 1 · 2018: 1 · 2017: 1 · 2016: 4 · 2015: 2
- FWCI
- 0.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- learning to rank, DCG, NDCG, graded relevance, active learning (machine learning)
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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