Personalized recommendation method and system based on probability model and user behavior analysis

The invention discloses a personalized recommendation method and system based on a probability model and user behavior analysis. The method includes the steps that article information and article attribute information are extracted, and operation behaviors of users on articles are extracted; interest points are obtained according to the article attribute information and the operation behaviors of the users on the articles; user interest similarity is obtained according to the operation behaviors of the users on the articles, and similar users are obtained; a decay factor is obtained according to the operation behaviors of the users on the articles based on the time dimension, and a user model is set up; interest characteristic information, at all dimensions, of the users is obtained according to the user model; after filtering, a recommendation algorithm is adopted to generate results to be recommended, and algorithm fusion is conducted to obtain personalized recommendation results of the users. After original data is preprocessed, the user model is set up, the interest points of the users and essential information acquisition requirements are depicted accurately to provide accurate personalized recommendation, and therefore the problems of information overload and long-tail articles in the network are solved.