Smart fuzzy querying based on user´s profile and context: A critical care planning system case study

Due to the hugeness of information stored in Web-databases, an overload phenomenon is often comes across when looking for sought after data. This phenomenon arises for instance when users, for example potential mobile physicians searching for adequate health care institutions for their patients in emergency cases, submit queries to old-fashioned recommender systems. To overcome this problem, we propose a new type of recommender systems that take both user profile and contextual preferences into account in order to retrieve and provide the best items to users´ interrogations. In this work, we use gradual queries which are based on fuzzy sets to model user´s preferences and context and a fuzzy inference machine to deduct new preferences. A case study related to a health care retrieval system is used to illustrate our proposal.