Eliminating the biases of user influence and item popularity in bipartite networks: A case study of Flickr and Netflix

User–item bipartite networks consist of users and items, where edges indicate the interactions of user–item pairs. We propose a Bayesian generative model for the user–item bipartite network that can measure the two types of rich-get-richer biases: item popularity and user influence biases. Furthermore, the model contains a novel measure of an item, namely the item quality that can be used in the item recommender system. The item quality represents the genuine worth of an item when the biases are removed. The Gibbs sampling algorithm alongside the adaptive rejection sampling is presented to obtain the posterior samples to perform the inference on the parameters. Monte Carlo simulations are performed to validate the presented algorithm. We apply the proposed model to Flickr user-tag and Netflix user–movie networks to yield remarkable interpretations of the rich-get-richer biases. We further discuss genuine item quality using Flickr tags and Netflix movies, considering the importance of bias elimination.