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Users' eye gaze pattern in organization-based recommender interfaces

Authors:

Li Chen,

Pearl PuAuthors Info & Claims

IUI '11: Proceedings of the 16th international conference on Intelligent user interfaces

Pages 311 - 314

https://doi.org/10.1145/1943403.1943453

Published: 13 February 2011 Publication History

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Abstract

In this paper, we report the hotspot and gaze path of users' eye-movements on three different layouts for recommender interfaces. One is the standard list layout, as appearing in most of current recommender systems. The other two are variations of organization interfaces where recommended items are organized into categories and each category is annotated by a title. Gaze plots infer that the organization interfaces, especially the quadrant layout, are likely to arouse users' attentions to more recommendations. In addition, more users chose products from the organization layouts. Combining the results with our prior works, we suggest a set of design guidelines and practical implications to our future work.

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Cited By

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Matsushita Y(2024)Gaze-Data-Based Probability Inference for Menu Item Position Effect on Information SearchJournal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2024.p030328:2(303-315)Online publication date: 20-Mar-2024
https://doi.org/10.20965/jaciii.2024.p0303
Zheng JWang H(2024)FSBPR: a novel approach to improving BPR for recommendation with the fusion of similarityThe Journal of Supercomputing10.1007/s11227-024-05911-6Online publication date: 6-Feb-2024
https://doi.org/10.1007/s11227-024-05911-6
Yan DChen L(2022)The Influence of Personality Traits on User Interaction with Recommendation InterfacesACM Transactions on Interactive Intelligent Systems10.1145/355877213:1(1-39)Online publication date: 24-Aug-2022
https://dl.acm.org/doi/10.1145/3558772
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Users' eye gaze pattern in organization-based recommender interfaces
1. Human-centered computing

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Published In

IUI '11: Proceedings of the 16th international conference on Intelligent user interfaces

February 2011

504 pages

ISBN:9781450304191

DOI:10.1145/1943403

General Chairs:
Pearl Pu
EPFL, Switzerland
,
Michael Pazzani
Rutgers University, USA
,
Program Chairs:
Elisabeth André
Augsburg University, Germany
,
Doug Riecken
CCLS Columbia University, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 February 2011

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IUI '11: 16th International Conference on Intelligent User Interfaces

February 13 - 16, 2011

CA, Palo Alto, USA

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Matsushita Y(2024)Gaze-Data-Based Probability Inference for Menu Item Position Effect on Information SearchJournal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2024.p030328:2(303-315)Online publication date: 20-Mar-2024
https://doi.org/10.20965/jaciii.2024.p0303
Zheng JWang H(2024)FSBPR: a novel approach to improving BPR for recommendation with the fusion of similarityThe Journal of Supercomputing10.1007/s11227-024-05911-6Online publication date: 6-Feb-2024
https://doi.org/10.1007/s11227-024-05911-6
Yan DChen L(2022)The Influence of Personality Traits on User Interaction with Recommendation InterfacesACM Transactions on Interactive Intelligent Systems10.1145/355877213:1(1-39)Online publication date: 24-Aug-2022
https://dl.acm.org/doi/10.1145/3558772
Chen LCai WYan DBerkovsky S(2022)Eye-tracking-based personality prediction with recommendation interfacesUser Modeling and User-Adapted Interaction10.1007/s11257-022-09336-933:1(121-157)Online publication date: 24-Jun-2022
https://doi.org/10.1007/s11257-022-09336-9
Millecamp MHtun NConati CVerbert K(2020)What's in a User? Towards Personalising Transparency for Music Recommender InterfacesProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394844(173-182)Online publication date: 7-Jul-2020
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Barreiros CSilva NPammer-Schindler VVeas E(2020)Nature at Your Service - Nature Inspired Representations Combined with Eye-gaze Features to Infer User Attention and Provide Contextualized SupportAdaptive Instructional Systems10.1007/978-3-030-50788-6_19(258-270)Online publication date: 19-Jul-2020
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Silva NBlascheck TJianu RRodrigues NWeiskopf DRaubal MSchreck TKrejtz KSharif B(2019)Eye tracking support for visual analytics systemsProceedings of the 11th ACM Symposium on Eye Tracking Research & Applications10.1145/3314111.3319919(1-10)Online publication date: 25-Jun-2019
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Pan WChen LMing Z(2019)Personalized recommendation with implicit feedback via learning pairwise preferences over item-setsKnowledge and Information Systems10.1007/s10115-018-1154-558:2(295-318)Online publication date: 1-Jun-2019
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Luo XSha CTan ZNiu J(2019)Multi-head Attentive Social RecommendationWeb Information Systems Engineering – WISE 201910.1007/978-3-030-34223-4_16(243-258)Online publication date: 29-Oct-2019
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Recommendations

Gaze Prediction for Recommender Systems

Intelligent gaze-added interfaces

Eye-Tracking study of user behavior in recommender interfaces

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