Multi-Behavior Enhanced Heterogeneous Graph Convolutional Networks Recommendation Algorithm based on Feature-Interaction

Figures & data

Figure 1. The user-item-feature heterogeneous tripartite graph.

Figure 2. Overall model architecture graph of ATGCN.

Table 1. Data set statistics.

Dataset	#Users	#Items feature	#Interactions
Amine	15506	34325 44	2601998
MovieLens-1m	6040	5953 18	1000209
Amazon-Book	51639	84355 11	2648963

Table 2. All evaluation indicators of the dataset amine and improvement of the optimal baseline.

Method	Recall@20	Recall@30	Recall@40	Recall@50	Precision@10	Precision@20	Ndcg@20
ItemCF	0.0286	0.0304	0.0401	0.0412	0.0260	0.0201	0.0115
MF	0.0354	0.0446	0.0524	0.0644	0.0311	0.0231	0.0214
NCF	0.0402	0.0542	0.0644	0.0685	0.0342	0.0256	0.0256
NGCF	0.0434	0.0580	0.0669	0.0772	0.0358	0.0294	0.0294
PUP PinSAGE	0.0482 0.0429	0.0574 0.0553	0.0684 0.0673	0.0784 0.0756	0.0336 0.0329	0.0296 0.0272	0.0304 0.0295
ATGCF	0.0535	0.0616	0.0702	0.0806	0.0372	0.0308	0.0320
ATGCN	0.0539	0.0621	0.0711	0.0815	0.0377	0.0313	0.0325
Improve	0.7%	0.8%	1.3%	1.1%	1.4%	1.6%	1.6%

Table 3. All evaluation indicators of the dataset MovieLens-1 m and improvement of the optimal baseline.

Method	Recall@20	Recall@30	Recall@40	Recall@50	Precision@10	Precision@20	Ndcg@20
ItemCF	0.0214	0.0238	0.0446	0.0512	0.0332	0.0327	0.0396
MF	0.0384	0.0425	0.0584	0.0748	0.0559	0.0445	0.0425
NCF	0.0401	0.0554	0.0644	0.0864	0.0532	0.0496	0.0489
NGCF	0.0443	0.0647	0.0839	0.1026	0.0604	0.0584	0.0504
PUP PinSAGE	0.0455 0.0431	0.0665 0.0607	0.0856 0.0812	0.1048 0.1015	0.0588 0.0496	0.0596 0.0478	0.0548 0.0537
ATGCF ATGCN	0.0482 0.0491	0.0689 0.0695	0.0884 0.0892	0.1070 0.1093	0.0645 0.0656	0.0627 0.0635	0.0581 0.0593
Improve	1.9%	0.9%	0.9%	2.2%	1.7%	1.3%	2.1%

Table 4. All evaluation indicators of the dataset Amazon-book and improvement of the optimal baseline.

Method	Recall@20	Recall@30	Recall@40	Recall@50	Precision@10	Precision@20	Ndcg@20
ItemCF	0.0184	0.0196	0.0312	0.0452	0.0186	0.0195	0.0356
MF	0.0250	0.0267	0.0524	0.0624	0.0254	0.0201	0.0518
NCF	0.0265	0.0288	0.0644	0.0665	0.0262	0.0225	0.0542
NGCF	0.0348	0.0387	0.0669	0.0731	0.0328	0.0257	0.0630
PUP PinSAGE	0.0382 0.0283	0.0415 0.0357	0.0684 0.0589	0.0754 0.0710	0.0316 0.0317	0.0285 0.0229	0.0624 0.0545
ATGCF ATGCN	0.0408 0.0416	0.0426 0.0433	0.0713 0.0722	0.0775 0.0783	0.0358 0.0367	0.0298 0.0309	0.0651 0.0658
Improve	1.5%	1.6%	1.3%	1.0%	2.5%	3.7%	1.1%

Figure 3. (a) The effect of diverse feature extraction of ATGCN on the model, (b) analytical user feature preferences by ATGCN at diverse interaction frequencies.

Figure 4. Different measurement results of the embedded dimensions of ATGCN key parameter characteristics on three data sets.

Figure 5. Different regularization coefficients of ATGCN on three data sets λ impact on results.

Figure 6. The recall and accuracy of ATGCN to the dataset MovieLens-1m under different training ratios.