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Recommender systems are promising ways to filter the overabundant informationin modern society. Their algorithms help individuals to explore decent items,but it is unclear how they allocate popularity among items. In this paper, wesimulate successive recommendations and measure their influence on thedispersion of item popularity by Gini coefficient. Our result indicates thatlocal diffusion and collaborative filtering reinforce the popularity of hotitems, widening the popularity dispersion. On the other hand, the heatconduction algorithm increases the popularity of the niche items and generatessmaller dispersion of item popularity. Simulations are compared to mean-fieldpredictions. Our results suggest that recommender systems have reinforcinginfluence on global diversification.

The reinforcing influence of recommendations on global diversification