  We consider the information filtering problem, in which we face a stream of
items, and must decide which ones to forward to a user to maximize the number
of relevant items shown, minus a penalty for each irrelevant item shown.
Forwarding decisions are made separately in a personalized way for each user.
We focus on the cold-start setting for this problem, in which we have limited
historical data on the user's preferences, and must rely on feedback from
forwarded articles to learn which the fraction of items relevant to the user in
each of several item categories. Performing well in this setting requires
trading exploration vs. exploitation, forwarding items that are likely to be
irrelevant, to allow learning that will improve later performance. In a
Bayesian setting, and using Markov decision processes, we show how the
Bayes-optimal forwarding algorithm can be computed efficiently when the user
will examine each forwarded article, and how an upper bound on the
Bayes-optimal procedure and a heuristic index policy can be obtained for the
setting when the user will examine only a limited number of forwarded items. We
present results from simulation experiments using parameters estimated using
historical data from arXiv.org.
