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Proceedings of the 14th ACM International Conference on Web Search and Data Mining
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Citations
Cited by
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Hort, M., Chen, Z., Zhang, J. M., Harman, M., & Sarro, F. (2022) · arXiv · doi:10.48550/arxiv.2207.07068 · Cited by 222
Dense Text Retrieval Based on Pretrained Language Models: A Survey
Zhao, W. X., Liu, J., Ren, R., & Wen, J.-R. (2024) · ACM Transactions on Information Systems · doi:10.1145/3637870 · Cited by 211
A/B testing: A systematic literature review
Quin, F., Weyns, D., Galster, M., & Silva, C. C. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112011 · Cited by 83
Can AI Provide Useful Holistic Essay Scoring?
Tate, T. P., Steiss, J., Bailey, D., Graham, S., Moon, Y., Ritchie, D., Tseng, W., & Warschauer, M. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100255 · Cited by 60
Provider fairness across continents in collaborative recommender systems
Gómez, E., Boratto, L., & Salamó, M. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2021.102719 · Cited by 39
Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction
Ma, X., Guo, J., Zhang, R., Fan, Y., & Cheng, X. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531772 · Cited by 35
Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives
Fu, Z., Niu, X., & Maher, M. L. (2024) · ACM Computing Surveys · doi:10.1145/3605145 · Cited by 30
Metadata
- Publisher
- ACM
- Published
- 2021-03-08
- OpenAlex
- W4206633947
- Cited by
- 318
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.