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ResearchFormerly WizWand

Web API Recommendation on ProgrammableWeb (test)

66.16PrecisionWARBERT
6.110421.700237.2952.8798Sep 27, 2025
Updated 6mo ago

Evaluation results

MethodLinks
WARBERTN=1, Backbone=BERT-Tiny, Number of Parameters=4.4M2025.0966.1645.166.1666.16
SEHGNN=1, patience=7, maximum epochs=1002025.0961.7342.9861.7361.73
MTFMN=12025.0958.9839.3958.9858.98
MTFM++N=12025.0958.6139.5758.6158.61
ServiceBERTN=1, batch size=32, fine-tuning learning rate=5 x 10^-52025.0958.0640.1958.0658.06
RWRN=12025.0942.9130.9742.9142.91
SPRN=12025.0941.8728.6841.8741.87
FC-LSTMN=12025.0930.8721.5330.8730.87
WARBERTN=5, Backbone=BERT-Tiny, Number of Parameters=4.4M2025.0921.6761.7271.7369.45
SEHGNN=5, patience=7, maximum epochs=1002025.0920.661.1463.8161.87
MTFM++N=52025.0919.455.1865.9563.06
MTFMN=52025.0919.255.3566.1363.24
RWRN=52025.0918.7654.4959.9755.1
SPRN=52025.0917.4448.7455.6851.51
ServiceBERTN=5, batch size=32, fine-tuning learning rate=5 x 10^-52025.0916.853.2952.7547.79
FC-LSTMN=52025.0913.2837.8442.7238.64
WARBERTN=10, Backbone=BERT-Tiny, Number of Parameters=4.4M2025.0912.1566.1571.8968.25
RWRN=102025.0911.5364.316254.71
MTFM++N=102025.0911.3661.2766.3761.64
SPRN=102025.0911.1459.757.8450.89
MTFMN=102025.0911.1160.6666.4461.74
SEHGNN=10, patience=7, maximum epochs=1002025.0910.4264.9864.0762.01
ServiceBERTN=10, batch size=32, fine-tuning learning rate=5 x 10^-52025.099.2557.3153.9648.18
FC-LSTMN=102025.098.4246.1445.2439.11