Mitigating Popularity Bias in Fair and Explainable Recommender Systems Using SHAP
Abstract
Popularity bias is a prevalent issue in recommendation systems, where popular items dominate recommendation lists, leading to reduced diversity and fairness. Traditional methods evaluate popularity bias based on overall item frequency, disregarding individual user tendencies. This study introduces a novel post-processing ranking method called Dynamic User Tendency Re-ranking (DUTR) to mitigate popularity bias in multi-criteria recommendation systems by incorporating user-specific preferences. DUTR leverages SHAP (SHapley Additive exPlanations) analysis to determine the influence of different criteria on user decision-making. Unlike conventional methods, which classify item popularity based on general trends, DUTR dynamically assesses each user's priority preferences. It then classifies items as popular or less popular based on individual preference patterns. This approach ensures that recommendation lists align more closely with user-specific interests while maintaining a balance between popular and less popular items. To validate the effectiveness of DUTR, extensive experiments were conducted on the YM10 and YM20 datasets. The results show that DUTR significantly reduces popularity bias while improving diversity and fairness in recommendations. Moreover, the integration of SHAP values enhances the explainability of the recommendation process, providing users with personalized and transparent suggestions. In conclusion, comparative analysis with existing techniques demonstrates that DUTR outperforms traditional methods in balancing popularity and personalization.
Keywords
Recommender systems, Popularity bias, User tendency, SHAP, Multi-criteria systems
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