Alleviating the unfairness of recommendation by eliminating the conformity bias

Abstract

Recommendation systems play a critical role in the Internet era. In the past decade, many methods have been proposed to improve the accuracy of recommendation. However, simply increasing the accuracy is usually accompanied by the popularity bias in the recommendation results. In addition, from the perspective of item providers the popularity bias will bring unfairness in exposure of different items to users. In this paper, we propose a method to alleviate problems of unfairness and popularity bias. The main idea is based on the hypothesis that some interactions of users with popular items are not due to their preferences, but attribute to the conformity propensity, which aggravates the popularity bias problem. Thus we consider to eliminate these so called “unreliable interactions” to alleviate the unfairness. We propose a two-step preferential diffusion process between conformity users and popular items in the random walk recommendation algorithm to reduce the effect of unreliable interaction and alleviate the popularity bias and unfairness under the condition of ensuring accuracy. The experiment results on the two benchmark datasets, Movie-Lens and Netflix, show that our algorithm can improve the fairness of recommendation results and alleviate the popularity bias while achieving slightly increased recommendation accuracy.