Click models are a central component of learning and evaluation in recommender systems, yet most existing models are designed for single ranked-list interfaces. In contrast, modern recommender platforms increasingly use complex interfaces such as carousels, which consist of multiple swipeable lists that enable complex user browsing behaviors. In this paper, we study position-based click models in carousel interfaces and examine optimization methods, model structure, and alignment with user behavior. We propose three novel position-based models tailored to carousels, including the first position-based model without latent variables that incorporates observed examination signals derived from eye tracking data, called the Observed Examination Position-Based Model (OEPBM). We develop a general implementation of these carousel click models, supporting multiple optimization techniques and conduct experiments comparing gradient-based methods with classical approaches, namely expectation-maximization and maximum likelihood estimation. Our results show that gradient-based optimization consistently achieve better click likelihoods. Among the evaluated models, the OEPBM achieves the strongest performance in click prediction and produces examination patterns that most closely align to user behavior. However, we also demonstrate that strong click fit does not imply realistic modeling of user examination and browsing patterns. This reveals a fundamental limitation of click-only models in complex interfaces and the need for incorporating additional behavioral signals when designing click models for carousel-based recommender systems.

翻译：点击模型是推荐系统学习和评估的核心组成部分，然而现有模型大多针对单一排序列表界面设计。相比之下，现代推荐平台日益采用复杂界面如轮播组件，其包含多个可滑动列表，支持复杂的用户浏览行为。本文研究轮播界面中的基于位置点击模型，探讨优化方法、模型结构及其与用户行为的对齐性。我们提出了三种专为轮播设计的创新基于位置模型，其中包括首个无需潜在变量、整合了源自眼动追踪数据的可观测检验信号的模型，称为可观测检验基于位置模型（OEPBM）。我们开发了这些轮播点击模型的通用实现方案，支持多种优化技术，并通过实验比较基于梯度的方法与经典方法（即期望最大化与最大似然估计）。实验结果表明，基于梯度的优化始终能获得更优的点击似然度。在评估的模型中，OEPBM在点击预测方面表现最佳，其生成的检验模式与用户行为最为吻合。然而，我们也证明强大的点击拟合度并不等同于对用户检验与浏览模式的真实建模。这揭示了复杂界面中纯点击模型的根本局限性，以及在设计基于轮播的推荐系统点击模型时整合额外行为信号的必要性。