RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces

2025-04-29

AI Summary

Carousel interfaces lack multimodal interaction datasets with eye-tracking, hindering research on visual-attention-driven recommender systems. To address this, we introduce RecGaze—the first open-source, eye-tracking–augmented dataset specifically designed for carousel scenarios. It comprises 3,477 interactions from 87 participants across three movie selection tasks, involving 40 distinct layout configurations; synchronized data include eye movements, clicks, mouse trajectories, and verbalized selection rationales. Our analysis reveals, for the first time in carousels, the emergence of both the “golden triangle” and F-shaped scanning patterns, and demonstrates statistically significant correlations between oculomotor features and interaction decisions (p < 0.001). RecGaze fills a critical gap in eye-tracking and multimodal behavioral research for carousel interfaces, providing an empirical foundation, analytical benchmark, and reproducible resource for gaze-aware recommendation systems.

Technology Category

• Computer Vision: Motion & Tracking
• Humans and AI: Interaction Techniques and Devices
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Consent frameworks and practices on the web
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Carousel interfaces are widely used in e-commerce and streaming services, but little research has been devoted to them. Previous studies of interfaces for presenting search and recommendation results have focused on single ranked lists, but it appears their results cannot be extrapolated to carousels due to the added complexity. Eye tracking is a highly informative approach to understanding how users click, yet there are no eye tracking studies concerning carousels. There are very few interaction datasets on recommenders with carousel interfaces and none that contain gaze data. We introduce the RecGaze dataset: the first comprehensive feedback dataset on carousels that includes eye tracking results, clicks, cursor movements, and selection explanations. The dataset comprises of interactions from 3 movie selection tasks with 40 different carousel interfaces per user. In total, 87 users and 3,477 interactions are logged. In addition to the dataset, its description and possible use cases, we provide results of a survey on carousel design and the first analysis of gaze data on carousels, which reveals a golden triangle or F-pattern browsing behavior. Our work seeks to advance the field of carousel interfaces by providing the first dataset with eye tracking results on carousels. In this manner, we provide and encourage an empirical understanding of interactions with carousel interfaces, for building better recommender systems through gaze information, and also encourage the development of gaze-based recommenders.

Problem

• Lack of eye tracking studies on carousel interfaces
• No existing datasets combine gaze data with carousel interactions
• Need empirical understanding of user behavior in carousel-based recommenders

Innovation

• First eye tracking dataset for carousels
• Includes gaze, clicks, cursor movements data
• Reveals golden triangle browsing behavior