An Empirical Comparison of Popularity-Debiasing Strategies in News and Short-Video Recommendation
Keywords:
popularity bias, news recommendation, short-video recommendation, debiasing evaluationAbstract
Popularity bias is a persistent obstacle in content recommendation: a small set of head items absorbs most exposure, long-tail items are systematically under-recommended, and offline metrics drift away from genuine user preference. A growing body of debiasing strategies has been proposed, yet most are evaluated on a single domain with a single backbone, leaving practitioners without clear guidance on which strategy to adopt under which conditions. This study presents a controlled empirical comparison of five representative popularity-debiasing strategies—inverse propensity scoring, popularity deconfounding and adjusting, model-agnostic counterfactual reasoning, interest–conformity disentangling, and automatic debiasing—applied uniformly across news and short-video recommendation. Each strategy is paired with strong domain backbones and evaluated on MIND, KuaiRand, and KuaiRec, using both accuracy metrics and bias-oriented metrics that exploit randomly exposed interactions for near-unbiased assessment. The results indicate that no single strategy dominates both domains: causal-intervention adjustment yields the most favorable accuracy–diversity balance on news, while disentangling-based representation learning is more effective on short-video data with random-exposure feedback. Across settings the accuracy cost of debiasing is moderate, while tail coverage improves substantially. We characterize the sensitivity of debiasing strength and the consistency of conclusions across datasets, and distill practical selection guidance for deployment.
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