From Latent to Observable Position-Based Click Models in Carousel Interfaces

2026-02-18

AI Summary

Existing click models are primarily designed for single-column ranked lists and struggle to capture the complex browsing and clicking behaviors of users in multi-column, horizontally scrollable carousel interfaces. This work proposes three novel position-dependent click models, among which OEPBM is the first to directly leverage eye-tracking data to construct observable exposure signals without relying on latent variables. Through a systematic comparison of optimization approaches—including gradient-based methods, EM, and maximum likelihood estimation—experiments demonstrate that OEPBM achieves superior click prediction performance and its inferred exposure patterns align most closely with actual user behavior. These findings underscore a fundamental limitation of conventional click-based models: they cannot accurately reflect users’ true examination processes when eye-tracking evidence is absent.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Evaluation and Analysis
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• click models
• carousel interfaces
• position-based models
• user examination
• recommender systems

Innovation

• carousel interfaces
• position-based click models
• observed examination
• gradient-based optimization
• eye tracking