AI 中文总结
针对轮播推荐评估指标N2DCG的两大局限，提出基于实证约束与用户浏览行为重构的N2DCG指标，经验证其更贴合用户行为与布局比较结果。
AI 中文摘要
轮播界面已广泛应用于视频和音乐流媒体服务，但如何在这类二维布局中正确评估推荐系统仍不明确。N2DCG被提出以弥合这一差距，它将NDCG适配到基于轮播的推荐中，但它依赖于从单列表网页搜索设置中借用的未经验证的假设，这些假设无法很好地迁移到二维轮播布局中。我们发现N2DCG存在两个重大局限：其用于归一化的理想排名违反了轮播约束，且其折扣函数未反映实证数据中观察到的用户浏览行为。为解决这两个局限，我们提出了N2DCG的重构版本，该版本通过尊重约束进行适当归一化，并使用基于实证的折扣函数。我们对所提出的指标进行了验证，结果表明它能更好地反映真实世界眼动追踪数据中的用户实证行为，且能更好地预测基于实证检查模式模拟的轮播布局的比较结果。
英文摘要
Carousel interfaces have been widely used in video and music streaming services, yet it remains unclear how to properly evaluate recommender systems in these two-dimensional layouts. N2DCG has been proposed to address this gap by adapting NDCG to carousel-based recommendation, but it relies on unverified assumptions borrowed from the single-list web-search setting that do not transfer well to two-dimensional carousel layouts. We identify two substantial limitations of N2DCG: its ideal ranking, used for normalization, violates carousel constraints, and its discount function does not reflect user browsing behavior observed in empirical data. To address both limitations, we propose a reformulation of N2DCG that normalizes appropriately by respecting constraints and uses an empirically grounded discount function. We validate the proposed metric, showing that it better reflects users' empirical behavior on real-world eye-tracking data and better predicts the comparison results of carousel layouts simulated based on empirical examination patterns.
