DBScholar

Back to papers

Equitable Top-k Results for Long Tail Data

Summary: Equitable top-k for long-tail data via theta-Equiv-top-k-MMSP; formalizes two subproblems: theta-Equiv-top-k-Sets and MaxMinFair. Generates many utility-equivalent top-k sets and a MaxMinFair distribution to equalize exposure; presents exact and scalable methods (random-walk, greedy, adaptive) and validates on six datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6804
Venue
SIGMOD
Year
2023
Pagerank
5.093636e-05
Overall Rank
11,419 | 21.66%
DOI
10.1145/3626727

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{islam_sigmod23,
        title = {{Equitable Top-k Results for Long Tail Data}},
        author = {Islam, Md Mouinul and Asadi, Mahsa and Roy, Senjuti Basu},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626727},
        url = {https://dl.acm.org/doi/10.1145/3626727},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,389 Fairness in Preference Queries: Social Choice Theories Meet Data Management 2024 VLDB 5.2755515e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers