Context-aware preference search for outdoor activity platforms

Complex application domains like outdoor activity platforms demand a powerful search interface that can adapt to personal user preferences and to changing contexts like weather conditions. Today most platforms offer a search technology known as Faceted Search, also named Parametric Search, where a user iteratively adapts his/her search parameters by a tedious and time-consuming trial-and-error process until the quality and quantity of the query results somehow corresponds to his/her expectations. This process gets even more cumbersome in mobile environments. Here we present a sophisticated approach called Preference Search, which we have prototypically implemented in a commercial outdoor activity platform. Preference Search replaces lengthy user sessions by one single user request. Technically, this request is automatically compiled into one single Preference SQL query, which efficiently retrieves those items that best match the user's expectations within the current context. A benchmark was applied to Faceted Search as well as Preference Search. The evaluation of the benchmark indicates that Preference Search substantially improves the user's search satisfaction in comparison to Faceted Search