A Temporal Model for Topic Re – Hotting Prediction in Online Social Networks
Abstract
A Temporal Model for Topic Re – Hotting Prediction in Online Social Networks,It is really popular to detect hot topics, which can benefit many tasks including topic recommendations, the guidance of public opinions, and so on. However, in some cases, people may want to know when to rehot a topic, i.e., make the topic popular again. In this paper, we address this issue by introducing a temporal user topic participation (UTP) model, which models users’ behaviors of posting messages. The UTP model takes into account users’ interests, friend-circles, and unexpected events in online social networks. Also, it considers the continuous temporal modeling of topics, since topics are changing continuously over time. Furthermore, a weighting scheme is proposed to smooth the fluctuations in topic rehotting prediction. Finally, experimental results conducted on real-world data sets demonstrate the effectiveness of our proposed models and topic rehotting prediction methods.
Conclusion
In proposed A Temporal Model for Topic Re – Hotting Prediction in Online Social Networks system is to solve the challenging problem of topic re-hotting prediction in OSNs. By taking into account three factors, i.e., users’ friend-circles, types of topics, and unexpected events, this system combines users interests and unexpected events. Furthermore, it use re-hot topic prediction algorithm for model inference and a Topic Mining within Region and Time Interval prediction method to predict the re-hotting time points accurately.
