## 4 Experiment
### Analysis and Discussion  
In this paper, we propose a TAUA model to provide accurate and diverse recommendation lists for e-commerce image recommendations. As can be seen from the results in Table 8, compared with the baseline model, the TAUA algorithm in this paper generally achieved the best performance in all evaluation indexes. Specifically, in the Amazon Product Data dataset, the F@10, NDCG@10, and AUC of our algorithm are 4.02%, 5.00%, and 2.143% higher than ACF, respectively, and 5.76%, 0.86%, and 1.16% higher than that of the VPOI. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **F@10** & **NDCG@10** & **AUC** \\ \hline TAUA & 0.0569 & 0.0587 & 0.7413 \\ CNN + UAN + TAA & 0.0532 & 0.0563 & 0.7258 \\ MCNN + UAN & 0.0557 & 0.0581 & 0.7306 \\ MCNN + TAA & 0.0548 & 0.0579 & 0.7295 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance ablation of TAUA in Amazon dataset. Figure 10: Comparison of top-k recommendation recall rate between image recommendation algorithm based on UAN and image recommendation algorithm based on users’ long-term and short-term preferences. On the Flickr dataset, our algorithm outperforms the ACF model by 5.74%, 7.565.12%, and 3.68% in F@10, NDCG@10, and AUC, respectively, and outperforms the VPOI model by 0.45%, 0.47%, and 0.49%. The TAUA algorithm is superior to VBPR in the recommendation performance, which shows that extracting deeper-level image information in the image recommendation task can better represent the image. The specific discussion and analysis are as follows:  
1. When k = 5, the proposed TAUA algorithm is slightly lower than the existing algorithm in terms of F and NDCG. As the number of recommendations increases, the performance of the TAUA algorithm is better than that of other algorithms. This is mainly due to the accuracy and diversity of our proposed algorithm. Since the accuracy and diversity are contradictory, MCNN and TAA reduce the recall to some extent, while UAN reduces the accuracy. Therefore, when the number of recommendations is small, there will be a large difference between accuracy and diversity. When the number of recommendations increases, accuracy and recall of the algorithm gradually tend to balance, both reach the highest simultaneously, and the algorithm performance reaches the optimum. 2. With an increase in k, the overall performance of the TAUA algorithm is better than VBPR, indicating that our algorithm is effective for image recommendations. The TAUA algorithm has made contributions in extracting image features and pixel context, obtaining more accurate image semantic descriptions, mining item features in a deeper and more comprehensive way and predicting user preferences. 3. The performance of the TAUA algorithm is better than other algorithms on the whole, which shows that the algorithm we proposed is effective. From the perspective of item and user, this paper extracts item features and user interests. Mainly, item features extracted through MCNN and TAA are used as the basis for judging user preferences, which reduces the deviation in understanding item and user preferences and ensures the consistency of data content utilization. Compared with BPR, LASSO, and VSM algorithms, which only consider the users' preference for an item, TAUA takes the style of the items as one important users' style preference, introduces the affiliation network, divides the users' historical behavior into the preference of different virtual users, and mines the users' implicit preferences. VBPR, DVBPR, and VPOI algorithms extract image features through CNN for recommendations, but the traditional CNN only focuses on the single dimension of the image; the TAUA algorithm extracts the category, color, and style features of the image through multiple channels and extracts features from more dimensions for recommendations. DVBPR and ACF divide the image into many regions and only pay attention to the local information of the image.