The reinforcing influence of recommendations on global diversification

By An Zeng, C. Yeung, Mingsheng Shang, Yi-Cheng Zhang. 2011

39 citations recorded by the source corpus.

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.

Subfields: Information Systems, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Barbara E. Engelhardt
2. Brandon M Stewart
3. Allison June-Barlow Chaney

In Statistical and Nonlinear Physics

1. Barbara E. Engelhardt
2. Allison June-Barlow Chaney
3. Brandon M Stewart

Citations in the corpus

• How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “Many efforts have been made to understand whether or not various recommender systems impact diversity by reinforcing the popularity of already-popular products or by increasing interactions with niche items [2, 11, 15, 19, 20, 46, 48, 50, 62, 67, 68].”