A distinctive feature of most advanced clinical decision support systems is the ability to adapt to habits and preferences of patients. However effective preferences elicitation is still among the most challenging tasks to achieve fully personalized guidance. On the other hand availability of data related to patients’ lives and habits is steadily increasing, making its exploitation an interesting opportunity for such purposes. In the MobiGuide project decision trees are used to implement shared-decision making using utility coefficients to incorporate patient preferences in the model. The main focus of this paper is the effort devoted to enhance traditional elicitation techniques proposing a methodology to predict patients’ health-related utility coefficients. In particular we describe a recommender system, based on collaborative filtering, capable of estimating utilities by means of integrating different data sources such as medical surveys, questionnaires and utility elicitation tools along with patient self-reported experiences in the form of natural language.