Information vs interaction: examining different interaction models over consistent metadata

In the quest to develop better and more useful search systems, many novel search user interface features have been developed, such as relevance feedback, clusters, tag clouds, facets, and so on. Yet all of these novel 'interactions' have required novel forms of 'information', or metadata, to make them work. Consequently, we do not know whether users have been benefiting from better interaction or simply richer forms of metadata, or both. In this research, we aimed to show that better interaction can be provided, regardless of whether we have access to, or the ability to generate, richer forms of metadata. Using only search engine query suggestions as a consistent form of metadata, we built interface conditions for three common interaction models for search: query suggestions (our baseline), hierarchical browsing, and faceted filtering. Our results showed that, despite interacting with the same underlying metadata, users experienced significant performance gains with different forms of interaction. These findings have implications for search user interface designers, who are often working with fixed metadata or within limited budgets. Our future work will focus on complementing these findings by recreating the same interaction with different forms of metadata, such that we can then compare the performance gain separately provided by both information and interaction.