## 4 Experiment
### Result  
The performance comparison of all methods on Amazon Product Data and Flickr datasets is shown in Table 8. Compared with other performances, the performance of the TAUA algorithm is not obvious when the number of recommendations is small, but it can effectively recommend more items. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Models** & **F05** & **F010** & **NDCG@5** & **NDCG@10** & **AUC** \\ \hline \multirow{8}{*}{Amazon} & BPR & 0.0241 & 0.0347 & 0.0393 & 0.0468 & 0.5310 \\  & VBPR & 0.0438 & 0.0512 & 0.0508 & 0.0541 & 0.7054 \\  & DVBPR & 0.0394 & 0.0355 & 0.0459 & 0.0517 & 0.6134 \\  & ACF & **0.0453** & 0.0547 & 0.0472 & 0.0559 & 0.7258 \\ \cline{1-1}  & VPOI & 0.0447 & 0.0538 & **0.0525** & 0.0582 & 0.7328 \\  & LASSO & 0.0218 & 0.0323 & 0.0377 & 0.0445 & 0.5218 \\  & VSM & 0.035 & 0.0563 & 0.0479 & 0.0578 & 0.717 \\  & TAUA & 0.0449 & **0.0569** & 0.0497 & **0.0587** & **0.7413** \\ \hline \multirow{8}{*}{Flickr} & BPR & 0.0177 & 0.0305 & 0.0242 & 0.0329 & 0.5543 \\  & VBPR & 0.0239 & 0.0439 & 0.0282 & 0.0317 & 0.6140 \\ \cline{1-1}  & DVBPR & 0.0196 & 0.0328 & 0.0254 & 0.0336 & 0.5642 \\ \cline{1-1}  & ACF & **0.0297** & 0.0418 & 0.0333 & 0.0410 & 0.6468 \\ \cline{1-1}  & VPOI & 0.0248 & 0.0440 & **0.0371** & 0.0429 & 0.6673 \\ \cline{1-1}  & LASSO & 0.0169 & 0.0296 & 0.0235 & 0.0314 & 0.5235 \\ \cline{1-1}  & VSM & 0.0262 & 0.0441 & 0.0356 & 0.0417 & 0.6560 \\ \cline{1-1}  & TAUA & 0.0268 & **0.0442** & 0.0324 & **0.0431** & **0.6706** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Algorithm performance comparison. Figures 7 and 8 show the NDCG of the proposed TAUA algorithm compared to other algorithms. Compared with VPOI and VSM, the TAUA algorithm also takes into account the pixel contextual information on the image and the users' affiliated user preference. The results show that the performance of TAUA is obviously better than other algorithms, which is more in line with the requirements of image recommendations. In order to prove the effectiveness of each module in the proposed TAUA algorithm, we carried out a comparative experiment. Specifically, MCNN is compared with dual-channel convolutional neural networks to compare their ability to classify image features. Compared with the two-channel convolutional neural network, the MCNN adds the style channel to extract the multi-dimensional features of the image. The experimental results are shown in Figure 9. The classification results of MCNN under different learning rates are shown in Table 9.  
Figure 8: Top-k image recommendations NDCG@k performance comparison on Flickr datasets. Figure 7: Top-k image recommendations NDCG@k performance comparison on Amazon datasets. In order to prove the accuracy of TAA in collecting pixel contextual information, we conducted a comparison experiment between TAA and CCNet. Table 10 proves that TAA is superior to CCNet in aggregating image pixel contextual information. Based on the extraction of users' long-term and short-term preferences, the affiliation network adds affiliated users, which not only considers indirect users but also sets up virtual characters. The image recommendation algorithm based on UAN is compared with the image recommendation algorithm based on users' long-term and short-term preferences. Figure 10 shows that UAN is effective in processing users' preferences, and it makes more effective use of users' historical behaviors to improve the diversity of recommendations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Method** & **mIOU (\%)** \\ \hline Amazon & CCNet & 81.3\% \\ Amazon & TAA & 82.1\% \\ Flickr & CCNet & 80.2\% \\ Flickr & TAA & 80.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison between TAA and CCNet aggregated pixel contextual information performance.