Recommender Systems (RSs) are tools for suggesting items that match the preferences and interests for a target user. These systems require the elicitation of customers preferences, which is not always precise because of external factors such as human errors, uncertainty, or vagueness proper of human beings. In RSs, such a problem is known as natural noise (NN) and can bias negatively the recommendations, leading to poor user's experience. The NN management has been addressed in previous works using crisp approaches. This contribution is devoted to a new fuzzy method for managing the uncertainty of NN in a exible and adaptable way for improving recommendations. A case study will show the improvements associated with the proposal.