In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various recommendation systems are designed not only to satisfy users' personalized information needs but also to allocate items' exposure. However, recent recommendation studies mainly focus on modeling user preferences to present satisfying results and maximize user interactions, while paying little attention to developing item-side fair exposure mechanisms for rational information delivery. This may lead to serious resource allocation problems on the item side, such as the Snowball Effect. Furthermore, unfair exposure mechanisms may hurt recommendation performance. In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items. We first conduct empirical analyses of feed scenarios to explore exposure problems between items with distinct uploaded times. This points out that unfair exposure caused by the time factor may be the major cause of the Snowball Effect. Then, we propose to explicitly model item-level customized timeliness distribution, Global Residual Value (GRV), for fair resource allocation. This GRV module is introduced into recommendations with the designed Timeliness-aware Fair Recommendation Framework (TaFR). Extensive experiments on two datasets demonstrate that TaFR achieves consistent improvements with various backbone recommendation models. By modeling item-side customized Global Residual Value, we achieve a fairer distribution of resources and, at the same time, improve recommendation performance.

翻译：在信息爆炸的时代，每天涌现大量新物品，尤其在信息流场景中。受限于有限的系统展示位和用户浏览注意力，各类推荐系统不仅要满足用户的个性化信息需求，还需分配物品的曝光机会。然而，近期推荐研究主要聚焦于建模用户偏好以呈现满意度最高的结果并最大化用户交互，鲜有关注开发面向物品侧的公平曝光机制以实现合理的信息分发。这可能导致物品侧严重的资源分配问题，例如"滚雪球效应"。此外，不公平的曝光机制可能损害推荐性能。本文呼吁将研究重点从用户偏好建模转向开发针对物品的公平曝光机制。我们首先对信息流场景进行实证分析，探索不同上传时间物品间的曝光问题，指出时间因素导致的不公平曝光可能成为"滚雪球效应"的主要成因。随后，我们提出显式建模物品级定制化时效性分布——全局残值（Global Residual Value, GRV），以实现公平资源分配。该GRV模块被引入推荐系统，并设计了时效感知公平推荐框架（TaFR）。在两个数据集上的大量实验表明，TaFR在多种骨干推荐模型上均能实现一致的性能提升。通过建模物品侧定制化全局残值，我们不仅实现了更公平的资源分配，同时提升了推荐性能。