Learning from Labeled Features for Document Filtering

TL;DR

This paper introduces a new method for document filtering that leverages user prior knowledge about important features, improving filtering accuracy by incorporating feedback on faceted features in semi-structured documents.

Contribution

The paper proposes a novel user profile learning algorithm that effectively integrates user feedback on features, enhancing document filtering performance over existing methods.

Findings

User feedback on faceted features improves filtering accuracy.

The proposed algorithm outperforms several existing methods.

User studies confirm the usefulness of feature-based feedback.

Abstract

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.