FACeTOR: cost-driven exploration of faceted query results

Abstract

Faceted navigation is being increasingly employed as an effective technique for exploring large query results on structured databases. This technique of mitigating information-overload leverages metadata of the query results to provide users with facet conditions that can be used to progressively refine the user's query and filter the query results. However, the number of facet conditions can be quite large, thereby increasing the burden on the user. We present the FACeTOR system that proposes a cost-based approach to faceted navigation. At each step of the navigation, the user is presented with a subset of all possible facet conditions that are selected such that the overall expected navigation cost is minimized and every result is guaranteed to be reachable by a facet condition. We prove that the problem of selecting the optimal facet conditions at each navigation step is NP-Hard, and subsequently present two intuitive heuristics employed by FACeTOR. Our user study at Amazon Mechanical Turk shows that FACeTOR reduces the user navigation time compared to the cutting edge commercial and academic faceted search algorithms. The user study also confirms the validity of our cost model. We also present the results of an extensive experimental evaluation on the performance of the proposed approach using two real datasets. FACeTOR is available at http://db.cse.buffalo.edu/facetor/.