SPROBID: Simplified PROBID using Top/Bottom Quartile Ideal Sets

Overview

Multi-ideal distance ranking with quartile-based ideal selection. Output typically ranking.

Strengths

• •Method-specific: Multi-ideal distance ranking with quartile-based ideal selection

Edge cases

• •See F.steps and D.parameters for SPROBID-specific edge handling. recommendation_metadata.assumptions_to_verify also lists boundary conditions.

Common pitfalls

• •Bkz. SPROBID F.steps citation_anchor'lar ve P.verification_status.

Worked example

1. 1.Adım 1 (F1): Steps F1-F4 identical to PROBID: vector normalization → weighted matrix → m ideal solutions → Euclidean distances Si and Si_average. Formül: r_{ij} = \frac{x_{ij}}{\sqrt{\sum_k x_{kj}^2}};\quad v_{ij}=w_j r_{ij};\quad S_{i(k)}=\sqrt{\sum_j(v_{ij}-A^{(k)}_j)^2} Anchor: Wang et al. 2021 §2 (SPROBID variant)
2. 2.Adım 2 (F2): Step F2 (SPROBID-specific): Use top ⌊m/4⌋ ideals for positive-ideal distance and bottom ⌊m/4⌋ for negative-ideal distance (quartile selection instead of PROBID's ⌈m/2⌉). S_pos_i = Σ_{k=1}^{⌊m/4⌋} S_i(k)/k; S_neg_i = Σ_{k=m+1-⌊m/4⌋}^{m} S_i(k)/(m-k+1). Final p_i = S_neg_i / S_pos_i. Rank descending. Formül: S^{pos}_{i}=\sum_{k=1}^{\lfloor m/4\rfloor}\frac{S_{i(k)}}{k};\quad S^{neg}_{i}=\sum_{k=m+1-\lfloor m/4\rfloor}^{m}\frac{S_{i(k)}}{m-k+1};\quad p_i=\frac{S^{neg}_i}{S^{pos}_i},\text{ rank descending} Anchor: Wang et al. 2021 §2 SPROBID variant

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