Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in "rich-get-richer" dynamics and a homogenization of visible content, but can also lead to misalignment of recommendations with individual users' preferences for popular or niche content. This work studies popularity bias through the lens of user-recommender alignment. To this end, we introduce Popularity Quantile Calibration, a measurement framework that quantifies misalignment between a user's historical popularity preference and the popularity of their recommendations. Building on this notion of popularity alignment, we propose SPREE, an inference-time mitigation method for sequential recommenders based on activation steering. SPREE identifies a popularity direction in representation space and adaptively steers model activations based on an estimate of each user's personal popularity bias, allowing both the direction and magnitude of steering to vary across users. Unlike global debiasing approaches, SPREE explicitly targets alignment rather than uniformly reducing popularity. Experiments across multiple datasets show that SPREE consistently improves user-level popularity alignment while preserving recommendation quality.

翻译：流行度偏差是推荐系统中的一个普遍问题，即推荐结果过度偏向流行物品。这不仅导致“富者愈富”的动态效应和可见内容同质化，还可能使推荐与用户对流行或小众内容的个人偏好产生偏差。本文从用户-推荐对齐的角度研究流行度偏差。为此，我们提出了流行度分位数校准这一衡量框架，用于量化用户历史流行度偏好与其推荐结果流行度之间的偏差。基于这种流行度对齐概念，我们提出了SPREE，一种基于激活引导的顺序推荐推理时缓解方法。SPREE在表征空间中识别流行度方向，并根据每个用户的个人流行度偏差估计自适应地引导模型激活，从而使得引导的方向和幅度因用户而异。与全局去偏方法不同，SPREE明确以对齐为目标，而非统一降低流行度。跨多个数据集的实验表明，SPREE在保持推荐质量的同时，持续提升了用户层面的流行度对齐效果。