Abstract: Traditional information push service takes little consideration of specific needs of social network users in particular conditions,hence it has poorly-targeted recommendations and low-rated system transformation.Responding to these problems,this paper proposed an intelligent push method based on user personas.By analyzing user data of intelligent learning platforms KNN clustering algorithm realized by KD-Tree is used to analyze user preferences and behavior characteristics,and then classifies user categories.First,through clustering center analysis,each type of users is abstracted into a highly-refined short text to form a representative label.Second,on account of label weight value of individual users and different service demands,user personas are modeled two times for refinement.Finally,recommendations are made by collaborative filtering algorithm.User personas will enhance the usability and value of user data.In addition,they may free analysts from large volumes of data,and help make fine classifications and thus more accurate recommendations.

Key words: Individualized recommendation, KD-Tree, Online learning resource push, User personas