Pattern-based Topics for Document Modelling in Information Filtering
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Pattern-based Topics for Document Modelling
in Information Filtering
Abstract— Pattern-based Topics for Document Modelling in Information FilteringMany mature term-based or pattern-based approaches have been used in the field of information filtering to generate users’ information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation < Final Year Projects 2016 > was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored.
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