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Enhancing recommendation diversity with organization interfaces

Published: 13 February 2011 Publication History

Abstract

Research increasingly indicates that accuracy cannot be the sole criteria in creating a satisfying recommender from the users' point of view. Other criteria, such as diversity, are emerging as important characteristics for consideration as well. In this paper, we try to address the problem of augmenting users' perception of recommendation diversity by applying an organization interface design method to the commonly used list interface. An in-depth user study was conducted to compare an organization interface with a standard list interface. Our results show that the organization interface indeed effectively increased users' perceived diversity of recommendations, especially perceived categorical diversity. Furthermore, 65% of users preferred the organization interface, versus 20% for the list interface. 70% of users thought the organization interface is better at helping them perceive recommendation diversity versus only 15% for the list interface.

References

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  • (2023)Examining the User Evaluation of Multi-List Recommender Interfaces in the Context of Healthy Recipe ChoicesACM Transactions on Recommender Systems10.1145/35819301:4(1-31)Online publication date: 24-Feb-2023
  • (2023)Synchronized Multi-list User Interfaces for Fashion CatalogsAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597382(224-228)Online publication date: 26-Jun-2023
  • (2023)The Influence of Personality Traits on User Interaction with Recommendation InterfacesACM Transactions on Interactive Intelligent Systems10.1145/355877213:1(1-39)Online publication date: 10-Mar-2023
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    cover image ACM Conferences
    IUI '11: Proceedings of the 16th international conference on Intelligent user interfaces
    February 2011
    504 pages
    ISBN:9781450304191
    DOI:10.1145/1943403
    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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    Publication History

    Published: 13 February 2011

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    Author Tags

    1. diversity
    2. interface
    3. recommender system
    4. user study

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    Cited By

    View all
    • (2023)Examining the User Evaluation of Multi-List Recommender Interfaces in the Context of Healthy Recipe ChoicesACM Transactions on Recommender Systems10.1145/35819301:4(1-31)Online publication date: 24-Feb-2023
    • (2023)Synchronized Multi-list User Interfaces for Fashion CatalogsAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597382(224-228)Online publication date: 26-Jun-2023
    • (2023)The Influence of Personality Traits on User Interaction with Recommendation InterfacesACM Transactions on Interactive Intelligent Systems10.1145/355877213:1(1-39)Online publication date: 10-Mar-2023
    • (2023)Enriching Recommender Systems Results with Data about Sustainability and Ethical Standards of Brands2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00037(238-242)Online publication date: 26-Oct-2023
    • (2022)Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System DesignProceedings of the ACM on Human-Computer Interaction10.1145/35556466:CSCW2(1-32)Online publication date: 11-Nov-2022
    • (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
    • (2021)“Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender InterfaceProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474232(124-132)Online publication date: 13-Sep-2021
    • (2021)Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’21)Proceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3470927(783-786)Online publication date: 13-Sep-2021
    • (2021)Diversify or Not: Dynamic Diversification for Personalized RecommendationAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-75765-6_37(461-472)Online publication date: 8-May-2021
    • (2021)Novelty and Diversity in Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_16(603-646)Online publication date: 22-Nov-2021
    • Show More Cited By

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