| Geotracker: geospatial and temporal RSS navigation |
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International World Wide Web Conference
archive
Proceedings of the 16th international conference on World Wide Web
table of contents
Banff, Alberta, Canada
SESSION: Smarter browsing
table of contents
Pages: 41 - 50
Year of Publication: 2007
ISBN:978-1-59593-654-7
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Authors
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Yih-Farn Robin Chen
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AT&T Labs, Florham Park, NJ
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Giuseppe Di Fabbrizio
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AT&T Labs, Florham Park, NJ
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David Gibbon
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AT&T Labs, Middletown, NJ
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Serban Jora
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AT&T, Florham Park, NJ
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Bernard Renger
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AT&T, Florham Park, NJ
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Bin Wei
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AT&T, Florham Park, NJ
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Downloads (6 Weeks): 36, Downloads (12 Months): 389, Citation Count: 4
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ABSTRACT
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.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 4
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Yih-Farn Chen , Giuseppe Di Fabbrizio , David Gibbon , Rittwik Jana , Serban Jora , Bernard Renger , Bin Wei, GeoTV: navigating geocoded rss to create an iptv experience, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada
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