We consider information filtering, in which we face a stream of items too
voluminous to process by hand (e.g., scientific articles, blog posts, emails),
and must rely on a computer system to automatically filter out irrelevant
items. Such systems face the exploration vs. exploitation tradeoff, in which it
may be beneficial to present an item despite a low probability of relevance,
just to learn about future items with similar content. We present a Bayesian
sequential decision-making model of this problem, show how it may be solved to
optimality using a decomposition to a collection of two-armed bandit problems,
and show structural results for the optimal policy. We show that the resulting
method is especially useful when facing the cold start problem, i.e., when
filtering items for new users without a long history of past interactions. We
then present an application of this information filtering method to a
historical dataset from the arXiv.org repository of scientific articles.Comment: 36 pages, 5 figure