User Mining Algorithm Based On PageRank Modeling Improvement and Comprehensive Influence Evaluation

摘要

Microblog has become the most popular medium of mass information dissemination. Users can know more friends in this virtual network through microblog's recommendation function. For example, Sina microblog, users can find like-minded people through the "add friends - interest" module, at present, microblog only recommends authenticated users or users' friends, and does not recommend most influential users to them. In view of the shortcomings mentioned above, this paper improves the PageRank modeling and adds the assessment of interaction and personal influence, thus improving the accuracy and comprehensiveness of the influential users' mining。 In order to verify the rationality and effectiveness of the algorithm, the real user data on Sina microblog based on Python grabbing were used for algorithm simulation, the experimental results show this algorithm has obvious advantages in terms of accuracy and recall rate when searching for potential influential users.