We explore various methods for predicting library book use, as
measured by circulation records.  Accurate prediction is invaluable
when choosing titles to be stored in an off-site location.  Previous
researchers in this area concluded that past use information provides
by far the most reliable predictor of future use. Because of the
computerization of library data, it is now possible not only to
reproduce these earlier experiments with a more substantial data set,
but also to compare their algorithms with more sophisticated decision
methods. We have found that while previous use is indeed an excellent
predictor of future use, it can be improved upon by combining previous
use information with bibliographic information in a technique that can
be customized for individual collections.  This has immediate
application for libraries that are short on storage space and wish to
identify low-demand titles to move to remote storage.  For instance,
simulations show that the best prediction method we develop, when used
as the off-site storage selection method for the Harvard College
Library, would have generated only a fifth as many off-site accesses
as compared to a method based on previous use.