Recommender systems are promising ways to filter the abundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they distribute popularity among items. In this paper, we simulate successive recommendations and measure their influence on the dispersion of item popularity by Gini coefficient. Our result indicates that local diffusion and collaborative filtering reinforce the popularity of hot items, widening the popularity dispersion. On the other hand, the heat conduction algorithm increases the popularity of the niche items and generates smaller dispersion of item popularity. Simulations are compared to mean-field predictions. Our results suggest that recommender systems have reinforcing influence on global diversification. Finally, the study of the hybrid method of mass diffusion and heat conduction reveals that the influence of recommender systems is actually controllable. Copyright © 2012 EPL Editorial Office.
conductive heat transfer