The purpose of feature extraction
Posted: Mon Dec 23, 2024 7:16 am
It can also be divided into static portraits and dynamic portraits. Static portraits refer to fixed and stable features and labels of content, such as theme, style, emotion, quality, length, format, type, and label. Dynamic portraits refer to changing and unstable features and labels of content, such as popularity, relevance, value, and influence. The dimension and depth of the personalized portrait of content determine the accuracy and effect of the personalized content page display. There are many methods for feature extraction. For example, you can use artificial intelligence large models, natural language processing, visual processing, multimodal processing, recommendation systems, and other technologies to automatically extract and annotate user and content features and labels.
of users and personalized portraits of content so as to israel cell phone number provide users with the most suitable content page display for them. The principle of feature extraction is to be as complete, accurate, new and as numerous as possible so as to improve the coverage, accuracy, efficiency and diversity of features. The difficulty of feature extraction is to solve problems such as the abstractness, implicitness, dynamics and diversity of features so as to ensure the quality and availability of features. 4. Model training After feature extraction, it is also necessary to train the user portrait and content portrait to learn the similarity and relevance between users and content, as well as the user's preferences and feedback on the display of different content pages.
Model training refers to the use of artificial intelligence large models to conduct deep learning and optimization of user portraits and content portraits so as to generate the most suitable content page layout plan for users and realize personalized content page display. The similarity and relevance between users and content refer to the degree of match and association between users and content. They determine the user's interest and demand for content, as well as the content's appeal and influence on the user. Generally speaking, the higher the similarity and relevance between users and content, the more interested the user is in the content and the more valuable the content is to the user.
of users and personalized portraits of content so as to israel cell phone number provide users with the most suitable content page display for them. The principle of feature extraction is to be as complete, accurate, new and as numerous as possible so as to improve the coverage, accuracy, efficiency and diversity of features. The difficulty of feature extraction is to solve problems such as the abstractness, implicitness, dynamics and diversity of features so as to ensure the quality and availability of features. 4. Model training After feature extraction, it is also necessary to train the user portrait and content portrait to learn the similarity and relevance between users and content, as well as the user's preferences and feedback on the display of different content pages.
Model training refers to the use of artificial intelligence large models to conduct deep learning and optimization of user portraits and content portraits so as to generate the most suitable content page layout plan for users and realize personalized content page display. The similarity and relevance between users and content refer to the degree of match and association between users and content. They determine the user's interest and demand for content, as well as the content's appeal and influence on the user. Generally speaking, the higher the similarity and relevance between users and content, the more interested the user is in the content and the more valuable the content is to the user.