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TU Berlin

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Information Retrieval and Social Network Analysis


Information Retrieval provides numerous challenges for Machine Learning: The sheer size of current document collections creates a high demand for tools that aid their navigation but also pushes the boundaries in terms of required algorithmic efficiency. At the same time, the only "ground truth" available is the relevance as perceived by the user, but user preferences may be a moving target. In this subject area, we are investigating different approaches to exploit hidden structures in user feedback and document contents. Furthermore, many structures in online information systems can be represented as graphs and can be mined for generating user profiles. Here, we are currently analysing datasets created by social web applications using hypergraphs for data representation. Applications include community detection, i.e. the identification of particularly well-connected subgroups, and the clustering of documents using tagging data.

Acknowledgements: Research was funded by DFG and Technische Universität Berlin.


Selected Publications:

Neubauer, N. and Obermayer, K. (2011). Tripartite community structure in social bookmarking data. New Review of Hypermedia and Multimedia, 17, 267-294.

Neubauer, N. and Obermayer, K. (2010). Community Detection in Tagging-Induced Hypergraphs. Workshop on Information in Networks, 24.- 25. September 2010, New York University, NY, USA, 1-5.,

Neubauer, N. and Obermayer, K. (2009). Hyperincident Connected Components in Tagging Networks. ACM SIGIWEB Newsletter, 229 – 238.

Neubauer, N., Scheel, C., Albayrak, A. and Obermayer, K. (2007). Distance Measures in Query Space: How Strongly to Use Feedback from Past Queries. Proceedings of the IEEE Conference on Web Intelligence 2007. IEEE, 607 – 613.,10.1109/WI.2007.65

Annies, R., Martinez, E., Adiloglu, K., Purwins, H. and Obermayer, K. (2007). Classification Schemes for Step Sounds Based on Gammatone-Filters. Proceedings of the IEEE Conference on Web Intelligence 2007,

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