Application of adaptive boosting (AdaBoost) in demand-driven acquisition (DDA) prediction: A machine-learning approach

Abstract

Demand-driven acquisition (DDA) programs are playing an increasingly important role in academic libraries. However, the literature surrounding this topic illustrates the wide-ranging, and frequently unpredictable, results of DDA implementation. As uncertainty abounds, librarians continue to seek out deeper understandings of those processes driving the use and purchase of DDA materials. Implicit in this search is a desire to understand how local environmental factors and user preferences dictate broader collection use and purchasing patterns. A small number of these studies have sought deeper insights through predictive modeling, though success has been limited. Following this line of inquiry, this study explores how machine learning might enable more effective collection development and management strategies through the predictive modeling of complex collection use and purchasing patterns. This research describes a replicable implementation of an adaptive boosting (AdaBoost) model that predicts the likelihood of DDA titles being triggered for purchase. The predictive capacity of this model is compared against a more traditional logistic regression model. This study's results show that the AdaBoost model possesses much higher predictive capacity than a regression-based model informed by the same set of predictors. The AdaBoost algorithm, once trained with local DDA data, provides accurate predictions in 82% of cases.