ATTention: Understanding Authors and Topics in Context of Temporal Evolution

Nasir Naveed, Sergej Sizov, Zahid Rauf


Understanding thematic trends and user roles is an important challenge in the field of information retrieval. In this paper, we worked on a novel probabilistic model for capturing the evolution of user’s interests in terms of content they generate over time. Our approach ATTention (a name derived from analysis of Authors and Topics in the Temporal context) addresses this problem by using of Bayesian modeling of relations between authors, latent topics and temporal data. The results of our experiments with scientific publications datasets are presented. The results show that using the ATTention model, it is possible to capture the change in users’ interests over time. We also discuss opportunities of using the model in certain mining and recommendation scenarios.


Social Media Analysis; Data Mining; Topic Analysis

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