Real-time recommendation method based on browsing preferences mining

After analyzing the advantages and disadvantages of various algorithms and the main problems of the current recommended technology,this paper put forward a real-time recommendation method based on user's browsing preferences mining.The algorithm first used Bayesian Network(BN) to estimate user's preferences by analyzing his Web browsing history,and then generated recommendations by calculating the matching degree of user's preferences and the characteristics of goods.The experimental results indicate that this method can provide personal recommendations more accurately and efficiently.