用现有推荐系统组件，帮冷门优质内容获得更多曝光。
Bypassing the Popularity Bias: Repurposing Models for Better Long-Tail Recommendation
- 复用工业级推荐系统组件，不改架构提升长尾内容曝光。
- 线上大规模实验显示，冷门优质作者曝光量显著提升。
- 适合关注公平性、长尾内容分发的平台方与研究者。
推荐系统在塑造我们在线上接触的信息方面起着关键作用，影响着我们的信念、选择和行为。许多近期工作关注推荐系统的公平性，通常聚焦于确保所有用户或用户群体获得平等的信息与机会，促进内容多样性以避免信息茧房和回音室效应，增强透明度与可解释性，并遵循伦理与可持续实践。本文旨在实现在线内容平台上各出版商之间的更公平曝光分布，尤其关注那些生产高质量长尾内容却可能被不公平压制的创作者。我们提出一种新方法，通过复用现有工业级推荐系统中的组件，在保持高推荐质量的同时，为欠代表性的出版商提供有价值的曝光。为验证该方法的高效性，我们进行了大规模线上AB测试，报告了预期结果，并分享了该方法在生产环境中长期应用的若干洞察。
原文摘要 · Abstract (English)
Recommender systems play a crucial role in shaping information we encounter online, whether on social media or when using content platforms, thereby influencing our beliefs, choices, and behaviours. Many recent works address the issue of fairness in recommender systems, typically focusing on topics like ensuring equal access to information and opportunities for all individual users or user groups, promoting diverse content to avoid filter bubbles and echo chambers, enhancing transparency and explainability, and adhering to ethical and sustainable practices. In this work, we aim to achieve a more equitable distribution of exposure among publishers on an online content platform, with a particular focus on those who produce high quality, long-tail content that may be unfairly disadvantaged. We propose a novel approach of repurposing existing components of an industrial recommender system to deliver valuable exposure to underrepresented publishers while maintaining high recommendation quality. To demonstrate the efficiency of our proposal, we conduct large-scale online AB experiments, report results indicating desired outcomes and share several insights from long-term application of the approach in the production setting.
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