Table 3.

Results of the proposed WPLMF framework and baselines in Ours Dataset (The best performance is highlighted in bold)

Method	AUPR	F1	MMR	Precision@15	Recall@15	Precision	Recall
MCS-MKL [16]	0.6428 (1.3e−3)	0.6082 (1.2e−3)	10.1247 (1.4e−2)	0.6567 (7.6e−3)	0.7813 (1.9e−2)	0.6145 (1.2e−2)	0.6082 (1.1e−2)
FGRMF [6]	0.5117 (4.5e−3)	0.4993 (2.6e−3)	9.7799 (7.6e−2)	0.4932 (5.9e−3)	0.7842 (1.3e−2)	0.4953 (1.4e−2)	0.5043 (1.5e−2)
idse-HE [10]	0.5888 (1.1e−2)	0.5564 (1.1e−2)	8.128 1(7.4e−1)	0.5676 (8.5e−3)	0.9136 (1.3e−2)	0.4738 (3.5e−2)	0.6804 (3.9e−2)
Galeano’s [4]	0.6294 (2.8e−3)	0.5924 (1.9e−3)	10.0803 (3.4e−2)	0.6081 (5.2e−3)	0.7748 (1.1e−2)	0.6049 (1.9e−2)	0.5804 (1.2e−2)
Logit MF [7]	0.6124 (3.3e−3)	0.5766 (3.5e−3)	10.0619 (3.0e−2)	0.6370 (6.6e−3)	0.7263 (5.0e−3)	0.5819(1.3e−2)	0.5714 (1.6–3)
WPLMF (ours)	0.6553 (3.0e−3)	0.6095 (1.8e−3)	10.0918 (1.9e−1)	0.6440 (11.8e−2)	0.77981 (5.5e−3)	0.6172 (1.4e−2)	0.6024 (1.1e−2)