Three-way recommender systems based on random forests

Recommender systems attempt to guide users in decisions related to choosing items based on inferences about their personal opinions. Most existing systems implicitly assume the underlying classification is binary, that is, a candidate item is either recommended or not. Here we propose an alternate framework that integrates three-way decision and random forests to build recommender systems. First, we consider both misclassification cost and teacher cost. The former is paid for wrong recommender behaviors, while the latter is paid to actively consult the user for his or her preferences. With these costs, a three-way decision model is built, and rational settings for positive and negative threshold values alpha* and beta* are computed. We next construct a random forest to compute the probability P that a user will like an item. Finally, alpha*, beta*, and Pare used to determine the recommender's behavior. The performance of the recommender is evaluated on the basis of an average cost. Experimental results on the well-known MovieLens data set show that the (alpha*, beta*)-pair determined by three-way decision is optimal not only on the training set, but also on the testing set. (C) 2015 Elsevier B.V. All rights reserved.