While popularity bias is recognized to play a role in recommmender (and other ranking-based) systems, detailed analyses of its impact on user welfare have largely been lacking. We propose a general mechanism by which item popularity, item quality, and position bias can impact user choice, and how it can negatively impact the collective user utility of various recommender policies. Formulating the problem as a non-stationary contextual bandit, we highlight the importance of exploration, not to eliminate popularity bias, but to mitigate its negative effects. First, naive popularity-biased recommenders are shown to induce linear regret by conflating item quality and popularity. More generally, we show that, even in linear settings, identifiability of item quality may not be possible due to the confounding effects of popularity bias. However, under sufficient variability assumptions, we develop an efficient UCB-style algorithm and prove efficient regret guarantees. We complement our analysis with several simulation studies.

翻译：尽管流行度偏差被认为在推荐系统（及其他基于排名的系统）中发挥作用，但对其如何影响用户福祉的详细分析仍然缺乏。我们提出了一种通用机制，用以解释项目流行度、项目质量和位置偏差如何影响用户选择，以及它如何对各类推荐策略的集体用户效用产生负面影响。通过将该问题形式化为非平稳情境赌博机，我们强调了探索的重要性——并非为了消除流行度偏差，而是为了减轻其负面效应。首先，研究表明，朴素的流行度偏差推荐器通过混淆项目质量与流行度会导致线性遗憾。更一般地，我们证明，即便在线性设定中，由于流行度偏差的混杂效应，项目质量的可识别性可能无法实现。然而，在充分的变异性假设下，我们开发了一种高效的UCB风格算法，并证明了其高效遗憾保证。我们通过多项模拟研究对分析进行了补充。