## 4 Experiment
### Experimental Scheme  
The experiments in this study were conducted on a desktop computer with 8 GB of RAM, an Intel Core i5-8250 CPU, and an Nvidia RTX 2070 GPU. The operating system was Windows 11 64-bit, and the software was Python 3.9 and MySQL 8.0. The models were implemented based on Tensorflow2.6.0, and Adam optimizer was used for training. TAUA involves cross-domain recommendations; we referred to some cross-domain recommendation dataset settings based on previous work [59]. Specifically, for the Amazon dataset, since no images of items were given in the dataset and the label description cannot meet the needs of our experiments, we used crawler technology to obtain the original image of the commodity and the labels of category, color, and style to supplement the data information in the dataset. In addition, user's comment time in the dataset was approximately regarded as the interaction time between the user and item, which was regarded as the basis for holiday and season attributes. For the Flickr dataset, we crawled the interactive feedback between the user and the item. Since the labels in the OpenImages5 dataset did not contain descriptions of image color and style, we used the MCNN model pre-trained on the Amazon dataset to extract and supplement the color and style labels of the image. Since users usually did not give their own personal information in e-commerce recommendations, we used the user affiliation network to find the user identity of the main user and infer the users' gender, age, and other information. The dataset contained many kinds of items, so we made recommendations in the feature dimension through the image features and labels extracted by the algorithm. The datasets were further processed to create datasets suitable for our experiment. Users with less than 5 interactions and items with less than 10 interactions were removed from this article to ensure the validity of each user and item. In order to evaluate the performance of the proposed method, we designed different test schemes based on Table 4. First, we set different user sizes in the experiment. Sub-datasets with user sizes of 1000 were taken and represented as Amazon and Flickr. The data were randomly divided into the training set (80%) and the test set (20%), run five times, and the average value of the five experimental results was taken as the final evaluation value. MCNN vs. Dual CNN: In order to test the excellent classification ability of MCNN in extracting image features with the addition of style channels, we compared MCNN with dual-channel convolutional neural networks. Dual channel convolutional neural network extracted the category and color features of images, and MCNN introduced style features. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **User** & **Item** & **Interactions** & **Object Label** \\ \hline Amazon & 1000 & 13,748 & 109,856 & 500 \\ Flickr & 1000 & 9492 & 86,147 & 550 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of the processed datasets. First, data preprocessing was conducted, and images were reshaped to 200 \(\times\) 200 as the input of MCNN. The activation function adopted by each layer was Relu function, the learning rate was set to 0.00001, and the network was trained for a maximum of 100 iteration rounds. At the same time, in order to prevent overfitting and improve the generalization ability of the model, we used the dropout layer to normalize the data, and the ratio was set to 0.2. The network parameters of MCNN are shown in Tables 5 and 6. TAA vs. CCNet: To evaluate the accuracy and advance of target alternating attention, we compared the ability of the TAA and CCNet to extract pixel contextual information. CCNet collected contextual information by means of the cross, and TAA used target to obtain pixel contextual information. Hollow convolution was used in the convolution layer, and the step size of the output was set to 8.