Fairness and bias issues in recommender systems have garnered increasing attention, with notable focus on popularity bias, exposure bias, and item fairness affecting recommendation effectiveness.
Previous studies have explored user-specific biases, indicating significant utility disparities among user demographic groups, underlining the need to address these biases for enhanced fairness.
The 'grey-sheep' problem exemplifies mainstream bias within recommender systems, highlighting the challenges faced by niche-interest users in finding similar peers for better recommendations.
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