基于传播树的微博推荐模型.

The thesis proposed a new microblog recommendation model based on propagation trees to address the issue that the current microblog recommendation models haven’t considered the propagation feature. Firstly, it used the tree structure to represent the microblog propagation feature, with the content, time, and user features forming the nodes of the tree and the forward and comment relationships of the microblog as the edges of the tree. Then, it calculated the propagation path similarity and propagation layer similarity between the to-be-evaluated microblog propagation tree and each microblog propagation tree of the target user. Based on the association relation and hierarchical relation among nodes to quantify the structural similarity between the two propagation trees. Finally, it sorted all the to-be-evaluated microblogs according to their similarity and generated recommendation lists to achieve microblog recommendations. The experimental results show that the model improves by 13.0%, 9.6%, and 10.7% in precision, recall, and F1-value,respectively, compared with the microblog recommendation model without considering the propagation feature. The reasonable use of the microblog propagation feature can improve the reliability of recommendation results and enhance the user experience. [ABSTRACT FROM AUTHOR]