Filtering semi-structured documents based on faceted feedback

  • Authors:

  • Lanbo Zhang;Yi Zhang;Qianli Xing

  • Affiliations:

  • University of California, Santa Cruz, Santa Cruz, CA, USA;University of California, Santa Cruz, Santa Cruz, CA, USA;Tsinghua University, Beijing, China

  • Venue:
  • Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
  • Year:
  • 2011

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Abstract

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.