Understanding Faceted Search from Data Science and Human Factor Perspectives

Abstract

Faceted search has become a common feature on most search interfaces in e-commerce websites, digital libraries, government’s open information portals, and so on. Beyond the existing studies on developing algorithms for faceted search and empirical studies on facet usage, this study investigated user real-time interactions with facets over the course of a search from both data science and human factor perspectives. It adopted a Random Forest (RF) model to successfully predict facet use using search dynamic variables. In addition, the RF model provided a ranking of variables by their predictive power, which suggests that the search process follows rhythmic flow of a sequence within which facet addition is mostly influenced by its immediately preceding action. In the follow-up user study, we found that participants used facets at critical points from the beginning to end of search sessions. Participants used facets for distinctive reasons at different stages. They also used facets implicitly without applying the facets to their search. Most participants liked the faceted search, although a few participants were concerned about the choice overload introduced by facets. The results of this research can be used to understand information seekers and propose or refine a set of practical design guidelines for faceted search.