Predicting individual book use for off-site storage using decision trees

Abstract

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.