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 used to custom-tailor newsfeeds 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- and intra- document dynamics by considering how information evolves over time from article to article, as well as within individual articles. We review the results of a user study undertaken to gauge the value of the approach over 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 rev...

• Type: Conference
• Year: 2004
• Where: WWW
• Authors: Evgeniy Gabrilovich, Susan T. Dumais, Eric Horvitz
• Keywords: General Terms Algorithms, Information Storage, Internet Technology, Legacy Time-based Review, WWW 2004