重新定义推荐评估指标，让二维轮播推荐更真实反映用户行为。
Revisiting N2DCG: An Empirically Grounded Reformulation of Carousel Recommendation Evaluation
- 基于真实眼动数据重构评分归一化方式，符合轮播界面约束。
- 采用实证数据驱动的折扣函数，更贴合用户浏览习惯。
- 适用于视频音乐平台轮播推荐的评估，提升结果可信度。
轮播界面在视频与音乐流媒体服务中广泛应用，但如何评估此类二维布局下的推荐系统仍不明确。现有方法N2DCG虽尝试将单列表搜索中的NDCG适配至轮播场景，却依赖未经验证的假设，难以迁移至二维布局。本文指出N2DCG存在两大缺陷：其理想排序违反轮播布局约束，且折扣函数未反映真实用户浏览行为。为此，我们提出一种重新形式化的N2DCG，通过尊重布局约束进行合理归一化，并采用基于实证数据的折扣函数。实验验证表明，新指标在真实眼动数据上更准确反映用户行为，且能更好预测基于实证观察模式模拟的轮播布局对比结果。
原文摘要 · Abstract (English)
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.
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