Abstract
User models generated from personal data are becoming a powerful tool used by service providers to personalize their supply, or introduce new features by means of recommendations or prediction of user behavior, among other applications. However, traditional user modelling requires each service provider to acquire and manage their users' personal information and carry out complex modelling processes. In this paper, we present a comprehensive personal data framework that distributes the user modelling process so that the personal data scattered in heterogeneous sources is used to generate on-demand, dynamic user models that can be retrieved by service providers to meet their business needs. Our proposal has been validated in a financial services scenario.
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