System and method for dynamically predicting user behaviors under hot topics

Abstract

The invention claims to protect a system and method for dynamically predicting user behaviors under hot topics, belonging to the field of social network analysis. Based on the relationship network between users in social networks and past behaviors of the users, the users are divided into hotspot users and alternative users according to the topic participation time of the users; the timelines characteristics of the topics are integrated by a time discretization and time slicing method; and for the non-uniformity and sparseness of data in all stages of life cycles of the hot topics, a tensor decomposition-based predicting model is constructed. At the same time, in order to reflect the dynamic form of topic development, an incremental tensor decomposition model is introduced to predict user behaviors after the topics are subjected to time slicing, the user behaviors are dynamically predicted, and the topic development trend can be grasped based on the predicted user behaviors.

Description

technical field [0001] The invention relates to the field of social network analysis, in particular to dynamic prediction of user behavior based on tensor decomposition under hot topics. Background technique [0002] With the development of the Internet and under the conditions of the social network big data era, Weibo has become a platform for information sharing, dissemination and acquisition based on user relationships. By mining the interactive behavior data among microblog users, we can grasp the behavior of users in social networks and the rules of information dissemination. Mastering user behavior characteristics can not only help enterprises provide users with better products and services according to user behavior characteristics, but also develop personalized services for different users, thereby enhancing the competitiveness of their own enterprises. Moreover, by grasping the law of information dissemination, it can provide a theoretical basis for relevant depart...

AI Technical Summary

Problems solved by technology

However, the above user behavior predictions based on individual users and groups are static, and there are relatively few studies on dynamic prediction of user behavior based on hot topics

Moreover, due to the timeliness of the topic, there are still problems of data inhomogeneity and data sparseness at different stages of the topic, which brings great challenges to dynamic user behavior prediction

Images