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  <title>BibSonomy publications for /bibtex/27fdfdd8f02ef0c7a006a07f2169085cc/marcoalvarez</title>
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  <dc:date>2008-08-21T12:41:46+02:00</dc:date>

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<item rdf:about="http://www.bibsonomy.org/uri/bibtex/27fdfdd8f02ef0c7a006a07f2169085cc/marcoalvarez">
    <title>Ranking a stream of news</title>
    <link>http://www.bibsonomy.org/bibtex/27fdfdd8f02ef0c7a006a07f2169085cc/marcoalvarez</link>
    <dc:creator>marcoalvarez</dc:creator>
    <dc:date>2008-05-14T11:33:59+02:00</dc:date>
    <dc:subject>News </dc:subject>
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	      <div class="bmtitle">

  <a href="http://www.bibsonomy.org/bibtex/27fdfdd8f02ef0c7a006a07f2169085cc/marcoalvarez">Ranking a stream of news</a>
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  <span style="color:#555555;"> 
    Gianna M. <a href="http://www.bibsonomy.org/author/Del+Corso">Del Corso</a>         	     	 
        	  and Antonio <a href="http://www.bibsonomy.org/author/Gull%5C%27%7B%5Ci%7D">Gull\&#039;{\i}</a>         	     	 
        	  and Francesco <a href="http://www.bibsonomy.org/author/Romani">Romani</a>         	     	 
        	 </span> 
  <em>International Conference on World Wide Web</em>
    97--106
  (2005)
</div>
<span class="bmmeta">
  
  
        to
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        <a href="http://www.bibsonomy.org/user/marcoalvarez/News">News</a>
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        on 2008-05-14 11:33:59 </span></div>
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        <swrc:booktitle>International Conference on World Wide Web</swrc:booktitle><swrc:pages>97--106</swrc:pages><swrc:title>Ranking a stream of news</swrc:title><swrc:year>2005</swrc:year><swrc:keywords>News </swrc:keywords><swrc:date>2008-05-14 11:33:59.0</swrc:date><swrc:abstract>According to a recent survey made by Nielsen NetRatings, searching
	on news articles is one of the most important activity online. Indeed,
	Google, Yahoo, MSN and many others have proposed commercial search
	engines for indexing news feeds. Despite this commercial interest,
	no academic research has focused on ranking a stream of news articles
	and a set of news sources. In this paper, we introduce this problem
	by proposing a ranking framework which models: (1) the process of
	generation of a stream of news articles, (2) the news articles clustering
	by topics, and (3) the evolution of news story over the time. The
	ranking algorithm proposed ranks news information, finding the most
	authoritative news sources and identifying the most interesting events
	in the different categories to which news article belongs. All these
	ranking measures take in account the time and can be obtained without
	a predefined sliding window of observation over the stream. The complexity
	of our algorithm is linear in the number of pieces of news still
	under consideration at the time of a new posting. This allow a continuous
	on-line process of ranking. Our ranking framework is validated on
	a collection of more than 300,000 pieces of news, produced in two
	months by more then 2000 news sources belonging to 13 different categories
	(World, U.S, Europe, Sports, Business, etc). This collection is extracted
	from the index of comeToMyHead, an academic news search engine available
	online.</swrc:abstract><swrc:author>
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