并且随着人工智能技术的出现，机器学习等方法在模拟用户非线性决策过程、进

And with the emergence of artificial intelligence technology, machine learning and other methods have strong advantages in simulating the non-linear decision-making process of users and further exploring the influence mechanism of various factors on user behavior. Old recommendations are facing huge challenges, and personalized recommendation algorithms are hot. People expect that the website will actively push the user's interested information to provide personalized services by analyzing the user's behavior and habits. An accurate recommendation system can analyze user behaviors by mining user's multi-modal information, and make portraits of people of different genders, ages, and occupations, accurately analyze user interests and hobbies, and recommend information of interest to users To meet the individual needs of users. Use users' reading history data to predict future reading interests and user preferences, and provide forward-looking and personalized services for their reading recommendations, enhance the internal communication and stickiness between users and reading websites, and form a good communication feedback mechanism. <br>The personalized recommendation algorithm is mainly based on the expansion of collaborative filtering, including collaborative filtering based on item information and collaborative filtering algorithm based on user information. Make recommendations based on online book information, mark the basic attributes, categories, tags, etc. of the books, and through in-depth analysis of the item information, recommend books that are similar to the user's previous browsing records. Make recommendations based on the user's information, and characterize the user's preferences based on his behavior. Through what the user likes, to establish his preference model for this user, and then push personalized content to the user. In this way, personalized recommendations can be realized and users' online reading experience can be improved.

And with the emergence of artificial intelligence technology, machine learning and other methods in simulating the user's nonlinear decision-making process, and then in-depth exploration of various factors on the user's behavior mechanism has a strong advantage. Old recommendations face huge challenges, and personalized recommendation algorithms are hot. People expect the website to analyze the user's behavior habits, the user is interested in the information actively push users, to provide personalized services. An accurate recommendation system can analyze the user's behavior and portraits of people of different genders, ages and occupations by mining the user's multimodal information, accurately analyze the user's interests and hobbies, and recommend the information of interest to the user to meet the user's personalized needs. Use the user's reading history data to predict the future reading interests and user preferences, and provide forward-looking, personalized service for their reading recommendations, enhance the communication and viscosity of users and reading sites, and form a good communication feedback mechanism.<br>The personalized recommendation algorithm is mainly based on collaborative filtering extension, including collaborative filtering based on item information and collaborative filtering algorithm based on user information. Based on the network book information to make recommendations, the basic properties of books, categories, labels and so on, through the in-depth analysis of item information, for users to recommend books similar to his previous browsing records. Based on the user information to make recommendations, according to the user's behavior to describe his preferences. Build a model of a user's preferences by what they like, and then push personalized content to the user. In order to achieve personalized recommendations, improve the user's experience of online reading.

And with the emergence of artificial intelligence technology, machine learning and other methods have strong advantages in simulating the user's nonlinear decision-making process, and further exploring the influence mechanism of various factors on user behavior. The old recommendation is facing great challenges, personalized recommendation algorithm is hot. People are looking forward to the website through the analysis of users' behavior habits, the information that users are interested in actively push users to provide personalized services. An accurate recommendation system can mine users' multimodal information, analyze users' behavior, and make portraits of people of different genders, ages and occupations, accurately analyze users' interests and hobbies, and recommend users' interested information to meet users' personalized needs. Using the reading history data of users to predict the future reading interest and user preferences, and provide forward-looking, personalized services for their reading recommendation, enhance the communication and stickiness between users and the reading website, and form a good communication feedback mechanism.<br>Personalized recommendation algorithm is mainly based on collaborative filtering, including collaborative filtering based on item information and collaborative filtering based on user information. Based on the online book information to make recommendations, the basic attributes of books, categories, labels, etc. are labeled, through the in-depth analysis of the item information, to recommend books similar to his previous browsing records for users. Recommendation is based on user's information, and user's preference is described according to user's behavior. The user's preference model is established by what he likes, and then personalized content is pushed to the user. In order to achieve personalized recommendation, improve the user's online reading experience.<br>