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Motivated by the commonly used faceted search interface in e-commerce, this paper investigates interactive relevance feedback mechanism based on faceted document metadata. In this mechanism, the system recommends a group of document facet-value pairs, and lets users select relevant ones to restrict the returned documents. We propose four facet-value pair recommendation approaches and two retrieval models that incorporate user feedback on document facets. Evaluated based on user feedback collected through Amazon Mechanical Turk, our experimental results show that the Boolean filtering approach, which is widely used in faceted search in e-commerce, doesn't work well for text document retrieval, due to the incompleteness (low recall) of metadata assignment in semi-structured text documents. Instead, a soft model performs more effectively. The faceted feedback mechanism can also be combined with document-based relevance feedback and pseudo relevance feedback to further improve the retrieval performance.
Zhang et al. (Mon,) studied this question.