Research And Implementation Of Personalized News Recommendation System Based On Collaborative Filtering

With the rapid development of the Internet, the amount of information grew rapidly. Users gradually enter the era of overload information from the era of poor information. However, users often can not find the information they need under this situation. In order to make network users find the information they need conveniently, researchers put forward many methods:the portal site, a professional information source; classification catalogue, grouping popular web sites into categories; search engine, through which users can find the necessary information simply by typing some key words. However, the users’ requirements go beyong that, they often donot know what information they need. Personalized recommendation systems are designed to filter a large amount of content the users aren’t interested in and help them find their own potential favor, and it has been widely used. With the great success achieved in the electronic commerce, personalized recommendation starts gradually penetrating into news field. As the Internet steps into the big data age, it also gives personalized news reading a good development opportunity.Personalized news recommendation system has made considerable progress in the theorectical study, but there are still many problems to be solved:scalability, timeliness, cold start, data sparseness and so on. So this thesis focuses on efficient scalable personalized news recommendation system. The work of this thesis mainly reflects in the several aspects.1. Puts forwad a new similarity measure which combines behavior similarity and content similarity. The new similarity measure solves the problems that traditional similarity calculation method is inaccurate or can not be calculated, alleviating the data sparseness. 2. Proposes a new scalable clustering method for personalized news recommendation, and changes the central point selection method and the distance metrics, greatly improving the news recommendation system scalability.3. Combines time factor in the stage of similarity calculation and final recommendation in the personalized news recommendation system, and ensures the time feature of news recommended.4. The collaborative filtering recommendation system is implemented based on MapReduce model, making the system runing simultaneously. This improves the system scalability and makes the system adapt to the demand of mass news and mass users recommendation.5. Experiments the clustering method and personalized news recommendation method to determine the revelant parameters. Also, this thesis gives functional test to news recommendation system to verify the system.This thesis analyzes the current personalized recommendation technology and Hadoop cloud computing platform. For special personalized news recommendation field, this thesis proposes a new scalable clustering method and a new similarity measure and verifys the effectiveness of the algorithms. On this basis, we design and realize the personalized news recommendation system using MapReduce model. Finally we give a detailed testing and evaluation results.