Comparative Study of One-Class Classifiers for Item-based Filtering

Abstract

In this paper, we address the recommendation process as a one-class classification problem. One-class classification is an umbrella term that covers a specific subset of learning problems that try to induce a general function that can discriminate between two classes of interest, given the constraint that training patterns are available only from one class. Usually, users provide ratings only for items that they are interested in and belong to their preferences without to give information for items that they dislike. The problem in one-class classification is to make a description of a target set of items and to detect which items are similar to this training set. We conduct a comparative study of one-class classifiers from density, boundary and reconstruction methods. The experimental results show that one-class classifiers do not only cope with the problem of missing of negative examples but also, succeed to perform efficiently in the recommendation process.