User profile construction method for personalized access to data sources using multivariate conjoint analysis and collaborating filtering

Abstract

Current information systems provide access to multiple, distributed, autonomous and potentially redundant data sources. Their users may not know the sources they questioned, nor their description and content. Consequently, their queries reflect no more a need that must be satisfied but an intention that must be refined. The purpose of personalization is to facilitate the expression of users’ needs. It allows them to obtain relevant information by maximizing the exploitation of their preferences grouped in their respective profile. In this work, we present a collaborative filtering method based on a Multivariate Conjoint Analysis approach to get these profiles. The proposed strategy provides a representation of the users and of the items, according to their characteristics, on factorial plans; whereas, the collaborative approach predicts the missing preferences.