Using context for online customer re-identification.

Plain contextual approach does not always outperform un-contextual one.Comparison depends on effects of contextual variable on customers' behavior.Using different contexts for different users lead to best performance.We propose plain and \"smart\" contextual re-identification approaches.Framework for selecting the best performing contextual approach for re-identification. The task of re-identification consists of linking records for individuals with no identifying information to records with identifying information (i.e., name or social security number) in order to identify individuals within the anonymous data. This task is important for business since firms want to precisely identify consumers for several reasons, such as targeting advertisements to them or labeling them as fraudulent users. For these reasons, companies strive to improve their re-identification techniques. In addition, the re-identification task is relevant from a research prospective, and many algorithms and techniques have been proposed to improve existing re-identification models. However, no previous research has studied whether the use of contextual variables can improve re-identification performance. Context can be defined as the circumstances under which transactions take place. To date, contextual information (i.e., the time of day when or the location where digital data was created) has been used successfully in other modeling tasks such as in the recommender system domain, where its ability to improve the accuracy of lists of items suggested to website users has been demonstrated. Including contextual information in a re-identification model is not a trivial task for several reasons. In this paper, we discuss the main issues regarding the use of context for the re-identification task, namely, when incorporating context is expected to help re-identification and when it is expected to hurt. We propose contextual re-identification models and a framework for deciding when to use these and determining the best performing contextual method for the re-identification task. We test our contextual models using three different case studies. Our findings have a significant impact on expert and intelligent systems since they provide the first evidence of the possibility of including contextual variables for improving the results of the re-identification process. The results also have a relevant impact for businesses since they can help managers decide when and how to include a contextual variable into the re-identification task and contextualize subsequent actions after the re-identification task.