ATTention: understanding authors and topics in context of temporal evolution

Authors:
Nasir Naveed;Sergej Sizov;Steffen Staab
Affiliations:
Institute for Web Science and Technologies, University of Koblenz-Landau, Germany;Institute for Web Science and Technologies, University of Koblenz-Landau, Germany;Institute for Web Science and Technologies, University of Koblenz-Landau, Germany
Venue:
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Year:
2011

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Abstract

Understanding thematic trends and user roles is an important challenge in the field of information retrieval. In this contribution, we present a novel model for analyzing evolution of user's interests with respect to produced content over time. Our approach ATTention (a name derived from analysis of Authors and Topics in the Temporal context) addresses this problem by means of Bayesian modeling of relations between authors, latent topics and temporal information. We also present results of preliminary evaluations with scientific publication datasets and discuss opportunities of model use in novel mining and recommendation scenarios.