unreasonable and invalid data such as noise,

[rifattryo.ut11]

There are many methods for data enhancement. For example, the data generation, data augmentation, data adversarial and other technologies of artificial intelligence big models can be used to automatically generate and transform new samples and scenarios in the data. The purpose of data processing is to build personalized portraits of users and personalized portraits of content so as to provide users with the most suitable content page display for them. The principle of data processing is to be as fast as possible, as good as possible, as much as possible, and as little as possible to improve the efficiency and effectiveness of data.



The difficulty of data processing is to solve problems israel cell phone number such as complexity, diversity, dynamics, and uncertainty of data so as to ensure the quality and availability of data. 3. Feature extraction After data processing, it is also necessary to extract features from the data to build personalized portraits of users and personalized portraits of content. Feature extraction refers to the use of artificial intelligence large models to conduct deep learning and analysis of user data and content data to extract key features and labels of users and content so as to provide users with the most suitable content page display for them.



The personalized portrait of users refers to the process of segmenting and labeling users. It can be divided into static portraits and dynamic portraits.Static portraits refer to fixed and stable features and labels of users, such as age, gender, region, interests, occupation, education, and income. Dynamic portraits refer to changing and unstable features and labels of users, such as behavior, preference, emotion, demand, and scenario. The dimension and depth of the personalized portrait of the user determine the accuracy and effect of the personalized content page display. The personalized portrait of content refers to the process of segmenting and labeling content.