On exploiting static and dynamically mined metadata for exploratory web searching
Most Web Search Engines (WSEs) are appropriate for focalized search, i.e., they make the assumption that users can accurately describe their information need using a small sequence of terms. However, as several user studies have shown, a high percentage of search tasks are exploratory, and focalized search very commonly leads to inadequate interactions and poor results. This paper proposes exploiting static and dynamically mined metadata for enriching web searching with exploration services. Online results clustering, which is a mining task of dynamic nature since it is based on query-dependent snippets, is useful for providing users with overviews of the top results and thus allowing them to restrict their focus to the desired parts. On the other hand, the various static metadata that are available to a search engine (e.g., domain, language, date, and filetype) are commonly exploited only through the advanced (form-based) search facilities that some WSEs offer (and users rarely use). We propose an approach that combines both kinds of metadata by adopting the interaction paradigm of dynamic taxonomies and faceted exploration, which allows the users to restrict their focus gradually using both static and dynamically derived metadata. Special focus is given on the design and analysis of incremental algorithms for speeding up the exploration process. The experimental evaluation over a real WSE shows that this combination results to an effective, flexible, and efficient exploration experience. Finally, we report the results of a user study indicating that this direction is promising in terms of user preference, satisfaction, and effort.
web searching results clustering interactive information retrieval faceted and dynamic taxonomies