The reinforcing influence of recommendations on global diversification

Abstract

Recommender systems are promising ways to filter the overabundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they allocate 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.

arXiv comment6 pages, 6 figures

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• An improved HeatS+ProbS hybrid recommendation algorithm based on heterogeneous initial resource configurations
• Improving information filtering via network manipulation
• Information filtering in sparse online systems: recommendation via semi-local diffusion
• Modeling mutual feedback between users and recommender systems
• Spectral coarse graining for random walk in bipartite networks
• Do recommender systems benefit users?
• Adaptive social recommendation in a multiple category landscape
• Enhancing the long-term performance of recommender system
• Membership in social networks and the application in information filtering