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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.
Gabrilovich et al. (Mon,) studied this question.