Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models

2025-06-23

AI Summary

Existing click model taxonomies are outdated, hindering unified evaluation of probabilistic graphical models (PGMs) and neural networks (NNs), and failing to accommodate modern interfaces such as carousels—thereby constraining model innovation. To address this, we reconstruct the foundational theory of click model design and propose three mathematically grounded core design choices. Based on these, we introduce the first unified taxonomy that jointly supports both PGMs and NNs across single-list, grid, and carousel interfaces. This taxonomy overcomes traditional classification limitations by enabling systematic, cross-model-type and cross-interface categorization. It integrates probabilistic modeling, deep learning, and statistical behavioral analysis to support interpretable and scalable click behavior modeling. Furthermore, leveraging this framework, we derive a novel model design paradigm specifically tailored for carousel interfaces—providing both theoretical foundations and methodological pathways for click modeling in emerging interactive interfaces.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Graphical Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Click models are a well-established for modeling user interactions with web interfaces. Previous work has mainly focused on traditional single-list web search settings; this includes existing surveys that introduced categorizations based on the first generation of probabilistic graphical model (PGM) click models that have become standard. However, these categorizations have become outdated, as their conceptualizations are unable to meaningfully compare PGM with neural network (NN) click models nor generalize to newer interfaces, such as carousel interfaces. We argue that this outdated view fails to adequately explain the fundamentals of click model designs, thus hindering the development of novel click models. This work reconsiders what should be the fundamental concepts in click model design, grounding them - unlike previous approaches - in their mathematical properties. We propose three fundamental key-design choices that explain what statistical patterns a click model can capture, and thus indirectly, what user behaviors they can capture. Based on these choices, we create a novel click model taxonomy that allows a meaningful comparison of all existing click models; this is the first taxonomy of single-list, grid and carousel click models that includes PGMs and NNs. Finally, we show how our conceptualization provides a foundation for future click model design by an example derivation of a novel design for carousel interfaces.

Problem

• Update outdated click model categorizations for modern interfaces
• Compare PGM and NN click models using mathematical properties
• Develop a unified taxonomy for single-list, grid, and carousel interfaces

Innovation

• Mathematical properties guide click model design
• Taxonomy compares PGM and NN click models
• Novel design for carousel interfaces demonstrated