To optimize the ranking of the page content for content consumption platform is very important. The users’ clicks and feedback behavior when they are reviewing pages were analyzed and captured and the user’s forward and reverse click were defined and matched on their interest label and the theme of their click content. We defined the feedback on the user’s preference for content and had regression analysis of extracting the user’s history data based on logistic regression model, and we reordered the in the content recommendation flow by user clicks and feedback of realtime behavior. Taking Epinion data set as the testing data set, the experimental results show that the new algorithm can improve the data AUC more significantly than the single use of logistic regression model.