Location in Search

As users turn increasingly to handheld devices to find information, the research community has focused on real-time location signals (GPS signals) to improve search engine effectiveness. Location signals have been investigated for predicting businesses the user will frequent[3], assigning geographic coordinates to media files[1], and to improve mobile search ranking[2]. While the increased focus on real-time user location has produced excellent research, there remains a gap between the capabilities being developed in the research community, and the capabilities being developed by commercial search engines. The core of this discrepancy between the advances in research and advances in industry is understanding the user's location. The vast majority of research on user location assumes that the user's location is known, because the user has provided a GPS signal. For many systems, there is no GPS signal available. The user may choose not enable it, or the system chooses not to prompt the user for the location because doing so degrades the user experience. For these interactions, the system relies on the user's IP address for location information. Further, much of the current research uses public geocoded data such as Foursquare (http://www.foursquare.com visited June 2015), and Twitter (http://www.twitter.com visited June 2015). These data are an incomplete picture of places a user may visit, and are potentially biased in their representation of actual users. The information contained in these data is not the same type of information typically available to a commercial search engine. In this talk we discuss gaps between current research on location, and industry advances in using location signals to improve search results. We focus on user location as one example of a gap between research and development.