In this paper authors study the effect of recommender systems (CF) on sale diversity. Motivated by "Lorenz curve", They use Gini coefficient (G) for measuring the bias of a recommender system. First, they used the so-called "urn-model" to explore the biases analytically. In this setting a stochastic function ( e.g., sigmoid function) gives the probability that an item being recommended by the system at each point of time, based on the current marker share. THE MODEL SUGGESTS the average increase of concentration bias for different settings of the model.
Inductive bias refers to your suspicion that if the sun has risen for the last billion days in a row, then it may rise tomorrow as well. Since it is logically possible that the laws of physics will arbitrarily cease to work and that the sun will not rise tomorrow, coming to this conclusion requires an inductively biased prior.