Rank Aggregation with Proportionate Fairness-Reference-Cited by-同舟云学术

Rank Aggregation with Proportionate Fairness

Published:2022-06-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 International Conference on Management of Data
language:
Short-container-title:

Author:

Wei Dong¹,Islam Md Mouinul¹,Schieber Baruch¹,Basu Roy Senjuti¹

Affiliation:

1. New Jersey Institute of Technology, Newark, NJ, USA

Funder

National Science Foundation

Office of Naval Research

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3514221.3517865

Reference36 articles.

1. Akanksha Agrawal , Grzegorz Guspiel , Jayakrishnan Madathil , Saket Saurabh , and Meirav Zehavi . 2019 . Connecting the dots (with minimum crossings) . In 35th International Symposium on Computational Geometry (SoCG 2019). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik. Akanksha Agrawal, Grzegorz Guspiel, Jayakrishnan Madathil, Saket Saurabh, and Meirav Zehavi. 2019. Connecting the dots (with minimum crossings). In 35th International Symposium on Computational Geometry (SoCG 2019). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.

2. Aggregation of Partial Rankings, p-Ratings and Top-m Lists

3. Aggregating inconsistent information

4. A Difficulty in the Concept of Social Welfare

5. Designing Fair Ranking Schemes

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Promoting Fairness and Priority in Selecting k -Winners Using IRV;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. PreFAIR: Combining Partial Preferences for Fair Consensus Decision-making;The 2024 ACM Conference on Fairness, Accountability, and Transparency;2024-06-03

3. Semantic deep learning and adaptive clustering for handling multimodal multimedia information retrieval;Multimedia Tools and Applications;2024-05-25

4. Fairness in Ranking: Robustness through Randomization without the Protected Attribute;2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW);2024-05-13

5. FairMC Fair–Markov Chain Rank Aggregation Methods;Lecture Notes in Computer Science;2024