User Interfaces for Counteracting Decision Manipulation in Group Recommender Systems

Thi Ngoc Trang  Tran; Alexander Felfernig; Viet Man Le; Müslüm Atas; Martin Stettinger; Ralph Samer

User Interfaces for Counteracting Decision Manipulation in Group Recommender Systems

Thi Ngoc Trang Tran, Alexander Felfernig, Viet Man Le, Müslüm Atas, Martin Stettinger, Ralph Samer

Institut für Softwaretechnologie (7160)

Publikation: Konferenzbeitrag › Poster › Begutachtung

Abstract

In group recommender systems,decision manipulation refers to an attack in which a group member makes attempts to push his/her favorite options. In this paper, we propose user interfaces to counteract decision manipulation in group recommender systems. The proposed user interfaces visualize information dimensions regarding rating adaptations of group members at different transparency levels. The results show that the user interface at the highest transparency level best helps to discourage users from decision manipulation. Besides, the ability of the user interfaces to counteract decision manipulation differs depending on the dimensions represented in the user interfaces. The information dimensions regarding item ratings and group recommendations have the strongest impacts on preventing users from decision manipulation.

Originalsprache	englisch
Publikationsstatus	Veröffentlicht - Juni 2019
Veranstaltung	27th ACM Conference on User Modeling, Adaptation and Personalization - Larnaca, Zypern Dauer: 9 Juni 2019 → 12 Juni 2019

Konferenz

Konferenz	27th ACM Conference on User Modeling, Adaptation and Personalization
Kurztitel	UMAP 2019
Land/Gebiet	Zypern
Ort	Larnaca
Zeitraum	9/06/19 → 12/06/19

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@conference{c29984cf7ddc41a091e90f32ce7e1d29,

title = "User Interfaces for Counteracting Decision Manipulation in Group Recommender Systems",

abstract = "In group recommender systems,decision manipulation refers to an attack in which a group member makes attempts to push his/her favorite options. In this paper, we propose user interfaces to counteract decision manipulation in group recommender systems. The proposed user interfaces visualize information dimensions regarding rating adaptations of group members at different transparency levels. The results show that the user interface at the highest transparency level best helps to discourage users from decision manipulation. Besides, the ability of the user interfaces to counteract decision manipulation differs depending on the dimensions represented in the user interfaces. The information dimensions regarding item ratings and group recommendations have the strongest impacts on preventing users from decision manipulation.",

keywords = "group decision making, group recommender systems, user interfaces, decision biases, decision manipulation",

author = "Tran, {Thi Ngoc Trang} and Alexander Felfernig and Le, {Viet Man} and M{\"u}sl{\"u}m Atas and Martin Stettinger and Ralph Samer",

year = "2019",

month = jun,

language = "English",

note = "27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019 ; Conference date: 09-06-2019 Through 12-06-2019",

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TY - CONF

T1 - User Interfaces for Counteracting Decision Manipulation in Group Recommender Systems

AU - Tran, Thi Ngoc Trang

AU - Felfernig, Alexander

AU - Le, Viet Man

AU - Atas, Müslüm

AU - Stettinger, Martin

AU - Samer, Ralph

PY - 2019/6

Y1 - 2019/6

N2 - In group recommender systems,decision manipulation refers to an attack in which a group member makes attempts to push his/her favorite options. In this paper, we propose user interfaces to counteract decision manipulation in group recommender systems. The proposed user interfaces visualize information dimensions regarding rating adaptations of group members at different transparency levels. The results show that the user interface at the highest transparency level best helps to discourage users from decision manipulation. Besides, the ability of the user interfaces to counteract decision manipulation differs depending on the dimensions represented in the user interfaces. The information dimensions regarding item ratings and group recommendations have the strongest impacts on preventing users from decision manipulation.

AB - In group recommender systems,decision manipulation refers to an attack in which a group member makes attempts to push his/her favorite options. In this paper, we propose user interfaces to counteract decision manipulation in group recommender systems. The proposed user interfaces visualize information dimensions regarding rating adaptations of group members at different transparency levels. The results show that the user interface at the highest transparency level best helps to discourage users from decision manipulation. Besides, the ability of the user interfaces to counteract decision manipulation differs depending on the dimensions represented in the user interfaces. The information dimensions regarding item ratings and group recommendations have the strongest impacts on preventing users from decision manipulation.

KW - group decision making

KW - group recommender systems

KW - user interfaces

KW - decision biases

KW - decision manipulation

M3 - Poster

T2 - 27th ACM Conference on User Modeling, Adaptation and Personalization

Y2 - 9 June 2019 through 12 June 2019

ER -

User Interfaces for Counteracting Decision Manipulation in Group Recommender Systems

Abstract

Konferenz

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