Research on personalized clothing recommendation system based on AIGC

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The personalized suit recommends the task quantitative contrast

Method	HR@10	NDCG@10	Recall@10	Precision@10
FPITF	0.0476	0.0444	0.0265	0.0118
FHN	0.0868	0.0724	0.0089	0.0013
MF	0.1798	0.071	0.0401	0.027
VBPR	0.1926	0.0702	0.0395	0.0223
NGCF	0.2472	0.0934	0.0358	0.0149
HGAN	0.2345	0.1266	0.0628	0.0146
GRCN	0.2426	0.1129	0.0568	0.0464

Compatibility modeling ablation experiment quantitative comparison table

Method	Accuracy rate(%)
w/o Category	0.8324
w/o Bi-direction	0.9056
w/o Attention	0.8857
GRCN	0.9502

Calculation results of clothing interest

Clothing coding	Costume style	Color	Collar type	Profile	Fabric	Interest
1442	1	2	3	2	2	0.7612
1626	2	2	2	1	5	0.7605
1777	1	2	3	1	4	0.7567
1658	3	2	2	3	3	0.7472
1378	1	2	3	0	3	0.7358
1591	2	2	1	1	3	0.6928
1386	1	2	3	4	4	0.6831
1392	2	2	2	4	4	0.6745
1369	2	2	2	1	1	0.6604
2285	3	2	2	3	4	0.6506
1621	2	2	2	2	2	0.6496
1175	1	2	2	1	2	0.6348
1732	1	1	2	0	2	0.6074
2082	3	2	2	3	1	0.5307
1677	0	2	2	3	4	0.5257
1687	1	2	2	1	1	0.5306
1660	3	2	1	3	1	0.5114
1318	1	2	2	2	3	0.5013
1658	4	2	2	2	4	0.4695
1497	2	2	2	1	3	0.4382

The personalized suit recommends the results of the experiment

Method	HR@10	NDCG@10	Recall@10	Precision@10
w/o Bt	0.2146	0.0877	0.0562	0.0314
w/o Intralayer	0.2429	0.1101	0.0535	0.0396
w/o Tb	0.2377	0.1033	0.0524	0.0335
GRCN	0.2627	0.1154	0.0555	0.0363

The accuracy of different methods in the FITB task

Method	Accuracy rate(%)
Random	0.248
SiameseNet	0.4994
Bi-LSTM	0.6579
FHN	0.7707
NGNN	0.8167
HGAN	0.8783
GRCN	0.9189