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Existing document filtering systems learn user profiles based on userrelevance feedback on documents. In some cases, users may have prior knowledgeabout what features are important. For example, a Spanish speaker may only wantnews written in Spanish, and thus a relevant document should contain thefeature "Language: Spanish"; a researcher focusing on HIV knows an article withthe medical subject "Subject: AIDS" is very likely to be relevant to him/her.  Semi-structured documents with rich metadata are increasingly prevalent onthe Internet. Motivated by the well-adopted faceted search interface ine-commerce, we study the exploitation of user prior knowledge on facetedfeatures for semi-structured document filtering. We envision two facetedfeedback mechanisms, and propose a novel user profile learning algorithm thatcan incorporate user feedback on features. To evaluate the proposed work, weuse two data sets from the TREC filtering track, and conduct a user study onAmazon Mechanical Turk. Our experiment results show that user feedback onfaceted features is useful for filtering. The proposed user profile learningalgorithm can effectively learn from user feedback on both documents andfeatures, and performs better than several existing methods.

Learning from Labeled Features for Document Filtering