Existing document filtering systems learn user profiles based on user relevance feedback on documents. In some cases, users may have prior knowledge about what features are important. For example, a Spanish speaker may only want news written in Spanish, and thus a relevant document should contain the feature "Language: Spanish"; a researcher focusing on HIV knows an article with the medical subject "Subject: AIDS" is very likely to be relevant to him her. Semi-structured documents with rich metadata are increasingly prevalent on the Internet. Motivated by the well-adopted faceted search interface in e-commerce, we study the exploitation of user prior knowledge on faceted features for semi-structured document filtering. We envision two faceted feedback mechanisms, and propose a novel user profile learning algorithm that can incorporate user feedback on features. To evaluate the proposed work, we use two data sets from the TREC filtering track, and conduct a user study on Amazon Mechanical Turk. Our experiment results show that user feedback on faceted features is useful for filtering. The proposed user profile learning algorithm can effectively learn from user feedback on both documents and features, and performs better than several existing methods.
This paper differs from the prior work by focusing on a different task: adaptive information filtering. Some of the techniques we tried in this paper are motivated by the prior work. The new user profile learning algorithm proposed in this paper is motivated by @cite_9 , however, with significant differences. First, our algorithm is designed to incorporate two types of user feedback, that is, to learn from labeled instances and features simultaneously in order to fit the filtering task where users may provide mixed types of feedback. In our algorithm, we use a unified loss function to combine user feedback on both instances and features. Secondly, our model is designed to capture the sufficiency and necessity of user-labeled features. The assumption of our model is users can identify important features and an important feature should have a high correlation with the document label. To measure this correlation, we propose the concepts of sufficiency and necessity and explicitly capture them in our algorithm.
@cite_9
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Existing adaptive filtering systems learn user profiles based on users' relevance judgments on documents. In some cases, users have some prior knowledge about what features are important for a document to be relevant. For example, a Spanish speaker may only want news written in Spanish, and thus a relevant document should contain the feature "Language: Spanish"; a researcher working on HIV knows an article with the medical subject "Subject: AIDS" is very likely to be interesting to him her. Semi-structured documents with rich faceted metadata are increasingly prevalent over the Internet. Motivated by the commonly used faceted search interface in e-commerce, we study whether users' prior knowledge about faceted features could be exploited for filtering semi-structured documents. We envision two faceted feedback solicitation mechanisms, and propose a novel user profile learning algorithm that can incorporate user feedback on features. To evaluate the proposed work, we use two data sets from the TREC filtering track, and conduct a user study on Amazon Mechanical Turk. Our experimental results show that user feedback on faceted features is useful for filtering. The new user profile learning algorithm can effectively learn from user feedback on faceted features and performs better than several other methods adapted from the feature-based feedback techniques proposed for retrieval and text classification tasks in previous work.