Topic Models for Data Mining
This event took place on Friday 13 July 2012 at 12:00
Dr. Tomoharu Iwata NTT/University of Cambridge
This event took place on Friday 13 July 2012 at 12:00
A topic model is a probabilistic generative model for extracting a latent structure from discrete data such as text document. Topic models are successfully used in a wide variety of applications including information retrieval, collaborative filtering and image recognition. In this talk, first I will present basics of topic modelling, such as relations with other probabilistic models, and inference. Then, I will present three applications of topic modelling: social annotation data analysis, multi-scale dynamics analysis, and fashion coordinates recommendation, which I worked on recent years.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Social Software is...

Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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