Fairness-Aware Recommendations for Kazakhstani Gaming Video Content

Aibar Zhangabay, Medet Mukushev

2026

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DOI 10.1109/icecco67619.2026.11488783
Data confidence 70 medium
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Abstract

This study develops a fairness-aware recommendation system for YouTube gaming content addressing underrepresentation of Kazakh-language videos and emerging creators. A post-processing re-ranking approach was applied to content-based and collaborative filtering baselines using multiplicative boost factors for Kazakh content (1.25x) and emerging creators (1.25x), with channel diversity penalties. Evaluation on 700 videos from seven channels demonstrated 75 % Kazakh representation and 85 % emerging creator representation in top-100 recommendations, exceeding fairness targets while maintaining 98 % engagement retention. Crossvalidation confirmed algorithm stability with coefficient of variation below 0.05 for primary metrics.