  Recommender systems recommend objects regardless of potential adverse effects
of their overcrowding. We address this shortcoming by introducing
crowd-avoiding recommendation where each object can be shared by only a limited
number of users or where object utility diminishes with the number of users
sharing it. We use real data to show that contrary to expectations, the
introduction of these constraints enhances recommendation accuracy and
diversity even in systems where overcrowding is not detrimental. The observed
accuracy improvements are explained in terms of removing potential bias of the
recommendation method. We finally propose a way to model artificial
socio-economic systems with crowd avoidance and obtain first analytical
results.
