Extending Faceted Search to the General Web

Abstract

Faceted search helps users by offering drill-down options as a complement to the keyword input box, and it has been used successfully for many vertical applications, including e-commerce and digital libraries. However, this idea is not well explored for general web search, even though it holds great potential for assisting multi-faceted queries and exploratory search. In this paper, we explore this potential by extending faceted search into the open-domain web setting, which we call Faceted Web Search. To tackle the heterogeneous nature of the web, we propose to use query-dependent automatic facet generation, which generates facets for a query instead of the entire corpus. To incorporate user feedback on these query facets into document ranking, we investigate both Boolean filtering and soft ranking models. We evaluate Faceted Web Search systems by their utility in assisting users to clarify search intent and find subtopic information. We describe how to build reusable test collections for such tasks, and propose an evaluation method that considers both gain and cost for users. Our experiments testify to the potential of Faceted Web Search, and show Boolean filtering feedback models, which are widely used in conventional faceted search, are less effective than soft ranking models.