## 1 Introduction  
Faced with the huge amount of data information on the Internet, traditional acquisition methods, such as keyword search, social media, online forums and communities, advertising, and information push, have difficulties in meeting the needs of users [1, 2]. Using an image recommender system is the most effective way to solve this problem [3]. On the other hand, the rapid development of the Internet is accompanied by the problems of information overload and information scarcity [4]. According to Statista, the number of Internet users worldwide reached 4.901 billion in 2021, and the global Internet penetration rate reached 62.5%. In the global information system, the proportion of information waste is not less than 50%, and some disciplines even account for 80%. It takes a lot of time and energy to find the right information, and the cost of obtaining the information is increasing. Another problem caused by information overload in the Internet era is the information cocoon, which confines user thinking in the information tower guided by interest, especially in the image recommender system [5]. Images have a higher complexity as well. When extracting semantic features, certain irrelevant information tends to interfere with the actual intent of users, resulting in recommendation bias. The image recommender system focuses on recommending items of interest in user historical behavior but does not have the mechanism to recommend implied preferences to users, which will lead to the narrowing of user information reception and increase the possibility of group polarization. Users and images are the two key and main information sources in the field of image recommendations, so it is particularly important to dig deeper into the content of images and users' implicit preferences for personalized recommendations. Existing research has achieved some results in solving the problem of image features and the use of user data by merging multi-dimensional information. However, the current research ignores the features of users and items themselves and still faces some difficulties in image semantic feature extraction, how to collect image pixel contextual information, and how to deal with the information cocoon effect. The main manifestations are as follows:  
1. Inadequate extraction of image semantic features  
Image management first needs to obtain the dimensional information contained in the image, but e-commerce images contain complex and diverse semantic information. E-commerce images not only have the dimensions of category and color but also have other dimensions such as style. Each user also has their style preference when shopping. Style is a relatively stable characteristic, reflecting personal aesthetics and preferences expressed through items. The category and color of images describe explicit semantic information, and the style of images reflects other information. 2. The ability to capture pixel contextual information needs to be improved  
Existing image processing methods, such as fully convolutional network (FCN), have made some progress, but due to their fixed geometry, they are limited to local receptive fields and short-range contextual information [6]. These limitations have a significant negative impact on FCN-based methods due to insufficient contextual information. Existing studies have proposed some methods to aggregate contextual information, but these methods have inherent shortcomings. For example, the method based on extended convolution cannot generate intensive contextual information [7; 8]. The pool-based method cannot meet the requirement that different pixels require different contextual dependencies, and the criss-cross network (CCNet) is not comprehensive enough to extract critical information, and the semantic feature description is incomplete, resulting in lower accuracy [9; 10]. 3. Failure to pay attention to users' social adjoint relationships  
A real personalized recommendation is not equal to only recommending the content that users have previously been interested in.