ATTention: Understanding Authors and Topics in Context of Temporal Evolution

Nasir Naveed, Sergej Sizov, Zahid Rauf

Abstract


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.

Keywords


Social Media Analysis; Data Mining; Topic Analysis

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References


Blei DM, Ng AY, Jordan MI. (2003). Latent Dirichlet Allocation. J. Mach. Learn. Res. 3:993– 1022.

Cheng V, Li CH. (2008). Linked topic and interest model for web forums. In: Proceedings of the 2008

IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 1:

–284. IEEE Computer Society, Washington, DC, USA.

Rosen-Zvi M, Griffiths T. Steyvers M, Smyth P. (2004). The author-topic model for authors and

documents. In: Proceedings of the 20th conference on Uncertainty in artificial intelligence. UAI ’04, AUAI

Press, Arlington, Virginia, United States, pp. 487–494.

Wang X, McCallum A. (2006). Topics over time: a non-markov continuous-time model of topical trends.

In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data

mining. KDD ’06, ACM, New York, NY, USA, pp. 424–433.

Wang X, Mohanty N, McCallum A. (2005). Group and topic discovery from relations and text. In:

Proceedings of the 3rd international workshop on Link discovery. LinkKDD ’05, ACM, New York, NY,

USA, pp. 28–35.

Nigam K, McCallum AK, Thrun S, Mitchell T. Text classification from labeled and unlabeled documents

using em. Mach. Learn 39(2-3):103–134, 2000.

Wang C, Blei DM, Heckerman D. (2008). Continuous time dynamic topic models. In UAI’08, 579–586

Wang X, Mohanty N, McCallum A. (2005). Group and topic discovery from relations and text. In

Proceedings of the 3rd international workshop on Link discovery, LinkKDD 05, New York, NY, USA,

ACM, pp28–35,



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