Evaluation and user preference study on spatial diversity

Abstract

Spatial diversity is a relatively new branch of research in the context of spatial information retrieval. Although the assumption that spatially diversified results may meet users' needs better seems reasonable, there has been little hard evidence in the literature indicating so. In this paper, we will show the potentials of spatial diversity by not only the traditional evaluation metrics (precision and cluster recall), but also through a user preference study using Amazon Mechanical Turk. The encouraging results from the latter prove that users do have strong preference on spatially diversified results. © 2010 Springer-Verlag Berlin Heidelberg.