DUoR: Dynamic User-oriented re-Ranking calibration strategy for popularity bias treatment of recommendation algorithms

Recommender systems are widely used to provide personalized recommendations to users to help them navigate the vast amount of available content. They have become pervasive in various online applications. However, they often suffer from popularity bias, where popular items receive more recommendations, leading to potential issues such as limited diversity, homogenized user experience, perpetuating existing inequalities, and filter bubble effects. In this paper, we propose a novel approach to mitigate popularity bias by incorporating users’ inclination towards item popularity. The proposed method incorporates a practical popularity inclination measuring strategy considering the dynamic preference tendencies of individuals to capture their unique propensities towards item popularity better and to provide more calibrated referrals regarding expectations of individuals on item popularity. Experimental results on benchmark datasets demonstrate that our proposed method effectively mitigates popularity bias by generating more diverse and balanced recommendations compared to several benchmark post-processing methods and outperforming them in diversity and fairness metrics according to the Borda count system. Overall, the proposed method presents a promising approach to addressing popularity bias in recommender systems by incorporating users’ inclination towards item popularity and opens up potential directions for further research in the field.