Personalized recommendation method based on user behavior analysis

The characteristics of user's behavior in the real scene are analyzed, and a personalized recommendation method based on user behavior analysis is put forward. In the electronic commerce user behavior can be divided into clicking, purchasing, collecting, plussing shopping cart, etc. The current mainstream algorithm collaborative filtering algorithm can not deal with other acts in addition to the purchase behavior. Take the method based on the artificial rule and the improved hierarchical fusion model based on bagging, converting the problem to a two classification problem for predicting whether or not to buy and recommending to users. Experimental results show that the proposed method makes full use of the user's behavior information, avoiding the limitations of the traditional methods, so that the recommended effect is significantly improved.