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New CloseSort algorithm efficiently aggregates cyclic rank-two preferences

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm called CloseSort for efficiently aggregating cyclic rank-two preferences. This method addresses the NP-hard problem of converting pairwise probabilities into a maximum-likelihood list, particularly for rank-two tournaments. CloseSort utilizes a geometric approach based on edge vectors and polygon area to find the optimal order in O(n log n) time, significantly outperforming existing methods. AI

IMPACT This algorithm could improve the efficiency of LLM evaluation and recommendation systems by providing a faster method for aggregating preferences.

RANK_REASON The item is a research paper detailing a new algorithm for rank aggregation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CloseSort algorithm efficiently aggregates cyclic rank-two preferences

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The item is a research paper detailing a new algorithm for rank aggregation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xianquan Yan ·

    Close and Sort: Exact Maximum-Likelihood Aggregation of Cyclic Rank-Two Preferences

    Pairwise probabilities arise in rank aggregation, recommendation, and human or LLM evaluation, yet converting them into a maximum-likelihood list is a weighted Kemeny problem and is NP-hard in general. Known exact algorithms for rank-two tournaments use only comparison signs and …

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