## 2 Related Work
### Image Feature Data Extraction  
Multi-attribute image classification is multi-task learning (MTL). The simplest method is to split it into simple independent single tasks for learning and then merge the results [11]. This means using several different convolutional neural networks to learn information, such as category, color, and style, from item images. However, for item images, if multiple attributes contained in an image are decomposed into independent issues, the correlation between multiple attributes will be ignored. For example, low-level features such as the edges of images are shared among multiple attributes. Many scholars have studied multi-attribute image classification. [PERSON] et al. divided human body images into several regions and then extracted the color histogram features of these regions to classify them [12]. [PERSON] et al. integrated the low-level features of histograms of oriented gradient (HOG), speeded up robust features (SURFs), local binary patterns (LBPs), etc., and adopted Random Forest to achieve the classification of clothing multi-attribute images [13]. [PERSON] et al. constructed a deep learning framework--which recognizes multiple attributes jointly (DeepMAR)--model for pedestrian multi-attribute image classification [14]. [PERSON] et al. used a method based on the combination of unsupervised segmentation and convolutional neural networks for multi-attribute e-commerce image retrieval [15]. The development of neural network image feature extraction technology can well solve the inconvenience caused by manual extraction [16]. Most of the current feature extraction methods are based on convolutional neural networks, which are widely used in the field of computer vision [17; 18]. Their excellent characteristics, such as local connection, weight sharing, pooling operation, and multi-layer structure, have attracted the attention of many researchers. They automatically extract features from data through multi-layer nonlinear transformation, having strong expression ability and learning ability. [PERSON] et al. proposed a joint multi-task learning algorithm for better prediction of attributes in images using a deep convolutional neural network (CNN) [19]. Binary semantic attributes were learned through the multi-task CNN model. [PERSON] et al. proposed the AlexNet deep convolutional neural network and applied the convolutional neural network to the automatic extraction of image features, forming abstract high-level features by combining low-level single features of the image and then using classifiers such as Softmax to classify the extracted features [20]. [PERSON] et al. used convolutional neural networks to learn semantic information about clothing and jewelry images and then recommended clothing matching to provide users with matching suggestions [21]. Some studies extracted item image features by using a convolutional neural network in combination with recommendation models, alleviating data sparsity and cold start problems of recommender systems to a certain extent [22; 23]. Compared with the above method, which only considers the image features obtained by convolutional neural networks, [PERSON] et al. also utilized the aesthetic features in images to improve the recommendation effect [24]. [PERSON] et al. proposed a convolutional neural network model to learn the unified representation of users and images in social content networks so that the similarity between users and images can be measured and then recommend images [25]. The features extracted using traditional content-based recommendation algorithms include low-level features, such as color, textures, and high-level features, such as pyramid histogram of oriented gradients (PHOGs) [26]. [PERSON] et al. introduced basic image low-level feature representation technology for color, texture, and shape features, and they proposed that the image characteristics with a single feature may lead to unsatisfactory image retrieval ability [27].