## 4 Experiment
### Experimental Scheme
We used the ploy decay strategy, in which the initial learning rate was \(lr\times\left(1-\frac{iter}{max,iter}\right)^{power}\), where power = 0.9, momentum = 0.9, and weight attenuation is 0.00001. UAN vs. long-term and short-term preferences: In order to measure the effectiveness of the user affiliation network, we conducted a comparative experiment between UAN and the users' long-term and short-term preference model. On the basis of extracting users' long-term and short-term preferences, UAN structured a user affiliation network and increased the preferences of the affiliated user. The time of user comments was used as a basis for distinguishing between short long-term and short-term preferences and time \(t\). In the experiment, the number of GRU units was set to 32, the regularization coefficient was set to \(1\times 10^{-6}\), the vector representation dimension of users and items was set to 32, and the batch_size was set to 32. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Layer** & **Kernel Size** & **Kernel Number** & **Output Dimension** \\ \hline convolution layer 5 & \(5\times 5\) & 128 & \(12\times 12\times 128\) \\ pooling layer 5 & \(2\times 2\) & - & \(6\times 6\times 128\) \\ convolution layer 6 & \(5\times 5\) & 256 & \(6\times 6\times 256\) \\ Fc1 & - & - & 2048 \\ Fc2 & - & - & 512 \\ Fc3 & - & - & 12 \\ convolution layer 7 & \(5\times 5\) & 128 & \(12\times 12\times 128\) \\ pooling layer 7 & \(2\times 2\) & - & \(6\times 6\times 128\) \\ convolution layer 8 & \(5\times 5\) & 256 & \(6\times 6\times 256\) \\ Fc4 & - & - & 2048 \\ Fc5 & - & - & 3 \\ Fc6 & - & - & 256 \(\times\) 256 \(\times\) 256 \\ convolution layer 9 & \(5\times 5\) & 128 & \(12\times 12\times 128\) \\ pooling layer 9 & \(2\times 2\) & - & \(6\times 6\times 128\) \\ convolution layer 10 & \(5\times 5\) & 256 & \(6\times 6\times 256\) \\ Fc7 & - & - & 2048 \\ Fc8 & - & - & 512 \\ Fc9 & - & - & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The latter part of the MCNN parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Layer** & **Kernel Size** & **Kernel Number** & **Output Dimension** \\ \hline Convolution layer 1 & \(5\times 5\) & 32 & \(200\times 200\times 32\) \\ Pooling layer 1 & \(2\times 2\) & - & \(100\times 100\times 32\) \\ Convolution layer 2 & \(5\times 5\) & 64 & \(100\times 100\times 64\) \\ Pooling layer 2 & \(2\times 2\) & - & \(50\times 50\times 64\) \\ Convolution layer 3 & \(5\times 5\) & 128 & \  
Finally, in order to evaluate the performance difference between the TAUA and other algorithms, we compared our method with the following baselines, including BPR, VBPR, DVBPR, ACF, VPOI, LASSO, and VSM [58; 60; 61; 62; 63; 64; 65]:  
1. BPR is a classic top-N recommendation method, which makes ranking optimization for each user preference. It is based on user image interaction, regardless of visual characteristics. This approach is considered the strongest baseline in the field of recommendations;
2. VBPR is an extension of BPR; it uses pre-trained CNN to extract visual features of item images and further combines image features with potential features for recommendations. In this approach, each image is treated as a single object;
3. DVBPR is an end-to-end training image feature and recommendation model integrating CNN and MF models;
4. ACF models project-level and component-level attention with two attention networks, and it simultaneously uses images and image regions to achieve a better recommender system. In this method, each image is divided into many regions of the same size;
5. VPOI uses visual content to enhance POI recommendations, extracts features from images through CNN, and uses them to guide the learning process of potential features between users and POI;
6. LASSO builds personalized models for users based on their favorite images;
7. VSM has fine-grained semantic features of images, and each image contains many semantic objects to better model image representation and user preferences.