Faceted Search is a widely used interaction scheme in digital libraries, e-commerce, and recently also in Linked Data. Surprisingly, object ranking in the context of Faceted Search is not well studied in the literature. In this article, we propose an extension of the model with two parameters that enable specifying the desired answer size and the granularity of the sought object ranking. These parameters allow tackling the problem of
too big
or
too small
answers and can specify
how refined
the sought ranking should be. Then, we provide an algorithm that takes as input these parameters and by considering the hard-constraints (filters), the soft-constraints (preferences), as well as the statistical properties of the dataset (through various frequency-based ranking schemes), produces an object ranking that satisfies these parameters, in a transparent way for the user. Then, we present extensive simulation-based evaluation results that provide evidence that the proposed model also improves the answers and reduces the user’s cost. Finally, we propose GUI extensions that are required and present an implementation of the model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.