Inducing Content Based User Models with Inductive Logic Programming Techniques

Martin E. Müller

FGML 2001


Instead of collaboratively modeling user interests we use web document classifications in order to describe individual user models. Information relevance is expressed with respect to an underlying ontology of text categories. Logic expressions over semi-lattices are interpreted as horn clauses - thus allowing to prove different levels of interestingness. Furthermore, this approach presents a well-defined learning problem for inductive logic programming which yields inspectable user models that include sets of interest aspects. By using both explicit positive and negative feedback for both interest and explicit dis-interest we can use few examples to generate a larger set of labeled data for the learning task.


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