Abstract: In the social network environmentthe traditional personalized recommendation method of social network users' in-terest points has the problems of low prediction accuracy of network users' interest behavior and low coverage of users' social datawhich can not fully mine the temporal and spatial sequence characteristics of users' interest points.Thereforea personalized recommendation method of social network users' interest points based on probability matrix decomposition algorithm is proposed.In the model training pseudo-code groupthe numerical results related to the matrix probability mutation operator are calculated to achieve the physical segmentation of the social networkand the node modeling of the social network based on the probability matrix decomposition algorithm is completed.On this basisthe framework of personalized social network is builtand the results are mined according to the characteristics of users' interest behaviorsand the personalized users are selected to recommend nodesso as to complete the establishment of personalized recommendation method for users' interest points in social network.The practical test results show thatcompared with the traditional methodthe new personalized recommendation method can predict the interest behavior of network users with the highest accuracy of 100%and the coverage rate of social data of users is about 75%which improve the prediction accuracy of interest behavior of network users and the coverage rate of social data of usersand fully excavate the temporal and spatial sequence characteristics of interest points of social network users.

Key words: Behavioral characteristics, Interest point recommendation, Mutation operator, Probability matrix, Pseudocode group, Social network users, Space-time sequence, Sub-settlement method