Artificial Intelligence based systems may be used as digital nudging\ntechniques that can steer or coerce users to make decisions not always aligned\nwith their true interests. When such systems properly address the issues of\nFairness, Accountability, Transparency, and Ethics, then the trust of the user\nin the system would just depend on the system's output. The aim of this paper\nis to propose a model for exploring how good and bad recommendations affect the\noverall trust in an idealized recommender system that issues recommendations\nover a resource with limited capacity. The impact of different users attitudes\non trust dynamics is also considered. Using simulations, we ran a large set of\nexperiments that allowed to observe that: 1) under certain circumstances, all\nthe users ended accepting the recommendations; and 2) the user attitude\n(controlled by a single parameter balancing the gain/loss of trust after a\ngood/bad recommendation) has a great impact in the trust dynamics.\n