document: Geotracker : geospatial and temporal RSS navigation The Web is rapidly moving towards a platform for mass collaboration in content production and consumption . Fresh content on a variety of topics , people , and places is being created and made available on the Web at breathtaking speed . Navigating the content effectively not only requires techniques such as aggregating various RSS-enabled feeds , but it also demands a new browsing paradigm . In this paper , we present novel geospatial and temporal browsing techniques that provide users with the capability of aggregating and navigating RSS-enabled content in a timely , personalized and automatic manner . In particular , we describe a system called GeoTracker that utilizes both a geospatial representation and a temporal ( chronological ) presentation to help users spot the most relevant updates quickly . Within the context of this work , we provide a middleware engine that supports intelligent aggregation and dissemination of RSS feeds with personalization to desktops and mobile devices . We study the navigation capabilities of this system on two kinds of data sets , namely , 2006 World Cup soccer data collected over two months and breaking news items that occur every day . We also demonstrate that the application of such technologies to the video search results returned by YouTube and Google greatly enhances a user . s ability in locating and browsing videos based on his or her geographical interests . Finally , we demonstrate that the location inference performance of GeoTracker compares well against machine learning techniques used in the natural language processing\/information retrieval community . Despite its algorithm simplicity , it preserves high recall percentages .
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