document: Turning down the noise in the blogosphere In recent years , the blogosphere has experienced a substantial increase in the number of posts published daily , forcing users to cope with information overload . The task of guiding users through this flood of information has thus become critical . To address this issue , we present a principled approach for picking a set of posts that best covers the important stories in the blogosphere . We define a simple and elegant notion of coverage and formalize it as a submodular optimization problem , for which we can efficiently compute a near-optimal solution . In addition , since people have varied interests , the ideal coverage algorithm should incorporate user preferences in order to tailor the selected posts to individual tastes . We define the problem of learning a personalized coverage function by providing an appropriate user-interaction model and formalizing an online learning framework for this task . We then provide a no-regret algorithm which can quickly learn a user 's preferences from limited feedback . We evaluate our coverage and personalization algorithms extensively over real blog data . Results from a user study show that our simple coverage algorithm does as well as most popular blog aggregation sites , including Google Blog Search , Yahoo ! Buzz , and Digg . Furthermore , we demonstrate empirically that our algorithm can successfully adapt to user preferences . We believe that our technique , especially with personalization , can dramatically reduce information overload .
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