Predictive Spatial Search
In a typical spatial search problem, (mobile) users search for (stationary or mobile) entities that have spatial attributes. The user’s current location and/or the entities’ locations are considered to assess the relevance of the search result. On one hand, we believe that the user’s future location is more relevant to the search result than the current location. Hence, we study spatial search queries under predictive models of user locations. On the other hand and with the ubiquity of hand held devices, most users do not utilize the full power of spatial search and they do not know what to search for. Hence, we introduce a framework to answer the question: “given a user’s current and predicted locations, what would the user be interested in searching for and seeing as a query result?” More specifically, we propose a predictive spatial search approach that continuously monitors the user’s current location to: (1) predict the user’s future location and integrate this prediction efficiently in the spatial search query processing pipeline, and (2) predict the search keywords that are of relevance to the user, given the user’s location and context. The second type of prediction leverages the knowledge of existing search engines about the behavior of a global set of spatial search users and social media users. Such two-phase prediction capability will enable search engines to “pre-search” on behalf of their users and, thereby, leading to gains in user experience, search accuracy, and communication costs.
Spatial Search Specialist Meeting, Santa Barbara, December 8-9