## 3 Methodology
### Multi-Channel Convolutional Neural Network Architecture  
Given the problem that traditional convolutional neural networks cannot simultaneously extract features from multiple dimensions contained in e-commerce images, the network designed in this paper has multiple channels, each of which corresponds to the learning task of different dimensions of improving the feature extraction ability of multi-dimensional images. Each user has their color or style preferences when shopping (extracted by attention in the user preference extraction section below), so users not only pay attention to the categories of items but also pay attention to the color and style of items and other information. Multi-attribute image means that an e-commerce image contains multiple dimensions. For example, a skirt can be described as a black preppy skirt, a dress can be described as a white French dress, a T-shirt can be described as a white leisure T-shirt, and a handbag can be described as a white ins handbag. The example are shown in Table 1. Each dimension describes e-commerce images from different angles and levels [19]. For the three dimensions of e-commerce image category, color, and style, the extracted features contribute to the classification of these three dimensions. Some prior distributions or model parameters can be shared in the learning tasks of these three dimensions, and these prior distributions or model parameters can be transferred during the training process. Fine-tuning is a common method in migration learning. It refers to using existing parameters to initialize new network parameters and transfer some tasks in the pre-training model to other tasks. In this way, the network can start learning from a good initial point, which can greatly save time when training new tasks. Figure 2: Flow chart of TAUA. The MCNN is shown in Figure 3. The former part of the network, like the traditional network, has four convolutional layers, and each convolutional layer is connected with a pooling layer. The three dimensions share parameters in the first four convolution layers. From the fourth pooling layer, the network is divided into three channels. Each channel consists of two convolutional layers, one pooled layer, three fully connected layers, and a final Softmax classifier. The first channel is trained and classified according to the dimension of category, the second channel is trained and classified according to color, and the third channel is trained and classified according to style. Because the maximum pooling can learn the texture structure of the image, all network pooling modes are maximum pooling. In the latter part of the network, the network parameters of the three channels are the same, but the first channel outputs 12-dimensional vectors at the last fully connected layer, corresponding to 12 categories of the kind of dimension; the second channel outputs 3-D vectors, corresponding to the RGB value of the color dimension; the third channel outputs 12-dimensional vectors, corresponding to 12 categories of the style dimension. These three vectors will be input into three Softmax classifiers for classification, and the higher the output value, the greater the probability of belonging to the corresponding category. When an image is input, three learning tasks can be carried out simultaneously; that is, the categories of kind, color, and style of the image can be predicted simultaneously through three classifiers. This paper focuses on clothing, digital, and other items on JD, Taobao, Amazon, and other platforms. One-hot coding is carried out for the major categories. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item Image** & **Description** \\ \hline black preppy skirt \\ white French dress \\ white leisure T-shirt \\ white ins handbag \\ white ins handbag \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dimension description of item image.