In the last decade, algorithms for reputation systems are been widely developed in order to achieve correct ratings for products, services, companies, digital contents and people. We start from a comprehensive mathematical model for Collaborative Reputation Systems (CRSes), present in the literature and formally defined as a recurrence relation that generates a sequence of trust matrices, from which the reputation of the items and the raters can be derived. Even though this model can be applied to several scenarios, the focus of this work is related to its application in a real case, that is a cultural event scenario. More in detail, in cultural heritage environment, the data collected in an event represent the basic knowledge to be inferred. The main idea is to correctly use the available technology and data to give a reliable rate (reputation) for both visitors and artworks. These rates will be very useful to classify the visiting style of the visitors and to fix the artworks that have most attracted visitors.