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Assortment optimization research benchmarks · SOTA2 Research

Assortment optimization

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

SUSHI Preference Data Set (test)

80Worst CR

97

Feb 26, 2026

SUSHI Preference Data

87Worst Coverage Ratio

37

Feb 26, 2026

Assortment Optimization Dataset (test)

100Average Ratio

16

Jun 15, 2026

MC N=100 (test)

103.6Avg. Ratio

7

Jun 15, 2026

Exponomial (test)

GCN-based Index with Local Search (GILS)

100.1Average Ratio

7

Jun 15, 2026

GCN-based Integer Programming (GIP)

103.9Avg. Ratio

7

Jun 15, 2026

Synthetic Assortment Data unknown-choice-model case (N_test=2,000, Strans=20,000, Ntrain=20) (test)

GCN-based Integer Programming (GIP)

95.9Average Ratio

7

Jun 15, 2026

Synthetic Assortment Data unknown-choice-model case N_test=500 Strans=20,000 Ntrain=20 (test)

GCN-based Integer Programming (GIP)

96.1Average Ratio

7

Jun 15, 2026

MNL (Multinomial Logit) model Discrete

—

—Primary metric

0

Feb 26, 2026

Popular datasets

Synthetic Assortment Data sushi NL SUSHI Preference MNL (Multinomial Logit)

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