In a collaborative filtering recommender system context, users are matched with items liked by others who have similar interests. However, each person may evaluate items differently according to their own experiences and standards. Therefore, analyzing the degree of exigency of an individual with respect to others consuming the same items is relevant. We propose a fuzzy approach based on a measure of consensus among users considering ratings. The metric considers the distances between users and a central measure. A centroid of the ratings for each individual item is proposed as the benchmark from which people's exigency is measured as being more or less stringent than his peers. In addition, the method will allow us to identify people with different degrees of exigency towards a specific type of item which can facilitate a more relevant recommendation. The model is implemented in a real case dataset of restaurants from the Yelp platform.