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Probabilistic models for context in social media

  • This thesis presents novel approaches for integrating context information into probabilistic models. Data from social media is typically associated with metadata, which includes context information such as timestamps, geographical coordinates or links to user profiles. Previous studies showed the benefits of using such context information in probabilistic models, e.g.\ improved predictive performance. In practice, probabilistic models which account for context information still play a minor role in data analysis. There are multiple reasons for this. Existing probabilistic models often are complex, the implementation is difficult, implementations are not publicly available, or the parameter estimation is computationally too expensive for large datasets. Additionally, existing models are typically created for a specific type of content and context and lack the flexibility to be applied to other data. This thesis addresses these problems by introducing a general approach for modelling multiple, arbitrary context variables in probabilistic models and by providing efficient inference schemes and implementations. In the first half of this thesis, the importance of context and the potential of context information for probabilistic modelling is shown theoretically and in practical examples. In the second half, the example of topic models is employed for introducing a novel approach to context modelling based on document clusters and adjacency relations in the context space. They can cope with areas of sparse observations and These models allow for the first time the efficient, explicit modelling of arbitrary context variables including cyclic and spherical context (such as temporal cycles or geographical coordinates). Using the novel three-level hierarchical multi-Dirichlet process presented in this thesis, the adjacency of ontext clusters can be exploited and multiple contexts can be modelled and weighted at the same time. Efficient inference schemes are derived which yield interpretable model parameters that allow analyse the relation between observations and context.

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Metadaten
Author:Christoph Kling
URN:urn:nbn:de:kola-13973
Referee:Steffen Staab, Markus Strohmaier, Lars Schmidt-Thieme
Advisor:Steffen Staab
Document Type:Doctoral Thesis
Language:English
Date of completion:2016/11/28
Date of publication:2016/11/30
Publishing institution:Universität Koblenz-Landau, Universitätsbibliothek
Granting institution:Universität Koblenz-Landau, Campus Koblenz, Fachbereich 4
Date of final exam:2016/11/16
Release Date:2016/11/30
Number of pages:191
Institutes:Fachbereich 4 / Institute for Web Science and Technologies
Licence (German):License LogoEs gilt das deutsche Urheberrecht: § 53 UrhG