Biometric information fusion for web user navigation and preferences analysis: an overview

Abstract

Throughout the years having knowledge of Web users' interests, navigational actions and preferences has gained importance due to the objectives of organizations and companies. Traditionally this field has been studied from the Web Mining perspective, particularly through the Web Usage Mining (WUM) concept, which consists of the application of machine learning techniques over data originated in the Web (Web data) for automatic extraction of behavioral patterns from Web users. WUM makes use of data sources that approximate users' behavior, such as weblogs or clickstreams among others; however these sources imply a considerable degree of subjectivity to interpret. For that reason, the application of biometric tools with the possibility of measuring actual responses to the stimuli presented via websites has become of interest in this field. Instead of doing separate analyses, information fusion (IF) tries to improve results by developing efficient methods for transforming information from different sources into a single representation, which then could be used to guide biometric data fusion to complement the traditional WUM studies and obtain better results. This paper presents a survey of Biometric IF applied to the WUM field, by first defining WUM and its main applications, later explaining how the Biometric IF could be applied and finally reviewing several studies that apply this concept to WUM. (C) 2017 Elsevier B.V. All rights reserved.