Revisiting N2DCG: An Empirically Grounded Reformulation of Carousel Recommendation Evaluation

2026-08-22

AI Summary

论文针对二维布局推荐系统评价问题，指出N2DCG存在的局限性，并提出了一种新的改进方法，该方法通过遵守约束和使用基于实证的折扣函数来更好地反映用户行为。

Abstract

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.

Problem

• Carousel Recommendation
• Evaluation Metrics
• N2DCG
• User Browsing Behavior

Innovation

• Reformulation of N2DCG
• Empirically Grounded Discount Function
• Carousel Constraints