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.
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Naveed, N., Sizov, S., Staab, S. (2011). ATTention: Understanding Authors and Topics in Context of Temporal Evolution. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_82
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DOI: https://doi.org/10.1007/978-3-642-20161-5_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20160-8
Online ISBN: 978-3-642-20161-5
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