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

• URL: http://arxiv.org/abs/2504.20792v1
• Date: Tue, 29 Apr 2025 14:09:20 GMT
• Title: RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
• Abstract summary: We introduce the RecGaze dataset: the first comprehensive feedback dataset on carousels.<n>The dataset comprises of interactions from 3 movie selection tasks with 40 different carousel interfaces per user.<n>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.
• Score: 58.695627866883065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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