Automatic Recommendation System Based On User Interest Model News

Personalized service provides different services to its users, according to the characteristics of their information and creates different service strategies and service models. Personalized service collects and analyzes user information to learn their interest and behavior in order to achieve the purpose of the recommended initiatives. This article discusses the design of automatically news recommendation system and builds a model for user interest comply with user preferences.The system can build interest model for users based on their reading habits and behavior analysis. Based on user interest model, the system filters out the news, takes the initiative to recommend them to the user. Model based on user interest can automatically provide a great user-friendly process of reading the news, and reduce the burden of filtering news information.Areas of Research Interests:1. How to create and express user's interest modelThe article analyzes the main interest model in use, and proposes to express user interest by news preferences, in accordance with the characteristics of the news information.2. How to determine the text of news complies with user interest model Successfully applying text categorization algorithm based on probability analysis.This algorithm establishes the relationship between the text keywords and the belonging categories, namely, text classification vector. According to text classification vector and the user's interest model, system determines whether the news information in line with user interest.3. How to update user interest modelThe system realizes the process of updating user interest model based on user feedback. The process may strengthen or weaken user interest in the model.