Dirichlet Mixture Model of Hawkes Processes Based Patent User Role Discovery Model

Abstract

With the complexity of patent transformation scenarios, the roles of users have become more diverse. Therefore, how to discover the roles of different users in the patent transformation scenarios has become a hot issue. In the process of patent transformation, the behaviors of each user are regular, historical behavior has an impact on the current behavior. Because the Hawkes processes can take into account the characteristic of self-exciting among behaviors, we explored the Dirichlet Mixture model of Hawkes Processes based on variational inference to cluster users for user roles discovery. In this model, different Hawkes processes correspond to different user types. Dirichlet distribution is used as the prior distribution of user clusters. The dependence of current behavior on historical behavior is expressed as intensity function. The variational inference is used to learn the model. The model is evaluated by Precision, Recall and F-measure, which shows that our model has good accuracy.