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Simplified Relative Citation Ratio for Static Paper Ranking: UFMG/LATIN at WSDM Cup 2016

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Sabir Ribas, Alberto Ueda, Rodrygo L. T. Santos, Berthier Ribeiro-Neto, Nivio Ziviani

Information Retrieval · Submitted 4 Mar 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim By using this single feature, which has no parameters and does not need to be tuned, our team was able to reach the 3rd position in the first phase of the WSDM Cup 2016.

how it is stated · asserted 1.00 read the record →

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arXiv ID1603.01336 (version 1)
Pith IDZCMJVMUH, Pith's permanent record of this paper and its review
CitationAlberto Ueda, Berthier Ribeiro-Neto, Nivio Ziviani, Rodrygo L. T. Santos, Sabir Ribas. "Simplified Relative Citation Ratio for Static Paper Ranking: UFMG/LATIN at WSDM Cup 2016." arXiv:1603.01336 (2016). https://pith.science/paper/ZCMJVMUH
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,535 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.51 · human participants 0.02 · controlled comparison 0.42 · randomized assignment 0.01 · states quantitative result 0.97 · compares with prior results 0.65 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Static rankings of papers play a key role in the academic search setting. Many features are commonly used in the literature to produce such rankings, some examples are citation-based metrics, distinct applications of PageRank, among others. More recently, learning to rank techniques have been successfully applied to combine sets of features producing effective results. In this work, we propose the metric S-RCR, which is a simplified version of a metric called Relative Citation Ratio --- both based on the idea of a co-citation network. When compared to the classical version, our simplification S-RCR leads to improved efficiency with a reasonable effectiveness. We use S-RCR to rank over 120 million papers in the Microsoft Academic Graph dataset. By using this single feature, which has no parameters and does not need to be tuned, our team was able to reach the 3rd position in the first phase of the WSDM Cup 2016.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Static rankings of papers play a key role in the academic search setting. background 1.00 0.04 0.03 asserted 1.00
s1 Many features are commonly used in the literature to produce such rankings, some examples are citation-based metrics, distinct applications of PageRank, among others. background 1.00 0.04 0.02 asserted 0.99
s2 More recently, learning to rank techniques have been successfully applied to combine sets of features producing effective results. background 1.00 0.05 0.03 asserted 1.00
s3 In this work, we propose the metric S-RCR, which is a simplified version of a metric called Relative Citation Ratio --- both based on the idea of a co-citation network. contribution 0.98 0.82 0.12 asserted 1.00
s4 When compared to the classical version, our simplification S-RCR leads to improved efficiency with a reasonable effectiveness. result 0.92 0.91 0.47 asserted 1.00
s5 We use S-RCR to rank over 120 million papers in the Microsoft Academic Graph dataset. method 0.91 0.95 0.07 asserted 1.00
s6 By using this single feature, which has no parameters and does not need to be tuned, our team was able to reach the 3rd position in the first phase of the WSDM Cup 2016. result 1.00 0.96 0.39 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.67.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Static rankings of papers play a key role in the academic search setting. Many features are commonly used in the literature to produce such rankings, some examples are citation-based metrics, distinct applications of PageRank, among others. More recently, learning to rank techniques have been successfully applied to combine sets of features producing effective results. In this work, we propose the metric S-RCR, which is a simplified version of a metric called Relative Citation Ratio --- both based on the idea of a co-citation network. When compared to the classical version, our simplification S-RCR leads to improved efficiency with a reasonable effectiveness. We use S-RCR to rank over 120 million papers in the Microsoft Academic Graph dataset. By using this single feature, which has no parameters and does not need to be tuned, our team was able to reach the 3rd position in the first phase of the WSDM Cup 2016.

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