document: Newsjunkie : providing personalized newsfeeds via analysis of information novelty We present a principled methodology for filtering news stories by formal measures of information novelty , and show how the techniques can be usedto custom-tailor news feeds based on information that a user has already reviewed . We review methods for analyzing novelty and then describe Newsjunkie , a system that personalizes news for users by identifying the novelty of stories in the context of stories they have already reviewed . Newsjunkie employs novelty-analysis algorithms that represent articles as words and named entities . The algorithms analyze inter-andintra-document dynamics by considering how information evolves over timefrom article to article , as well as within individual articles . We review the results of a user study undertaken to gauge the value of the approachover legacy time-based review of newsfeeds , and also to compare the performance of alternate distance metrics that are used to estimate the dissimilarity between candidate new articles and sets of previously reviewed articles .
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