Standard

Explanations for Groups. / Felfernig, Alexander; Tintarev, Nava; Tran, Thi Ngoc Trang ; Stettinger, Martin .

Group Recommender Systems. Cham : Springer, 2018. p. 105-126 (SpringerBriefs in Electrical and Computer Engineering).

Research output: ScientificChapter

Harvard

Felfernig, A, Tintarev, N, Tran, TNT & Stettinger, M 2018, Explanations for Groups. in Group Recommender Systems. SpringerBriefs in Electrical and Computer Engineering, Springer, Cham, pp. 105-126. DOI: 10.1007/978-3-319-75067-5_6

APA

Felfernig, A., Tintarev, N., Tran, T. N. T., & Stettinger, . M. (2018). Explanations for Groups. In Group Recommender Systems (pp. 105-126). (SpringerBriefs in Electrical and Computer Engineering). Cham: Springer. DOI: 10.1007/978-3-319-75067-5_6

Vancouver

Felfernig A, Tintarev N, Tran TNT, Stettinger M. Explanations for Groups. In Group Recommender Systems. Cham: Springer. 2018. p. 105-126. (SpringerBriefs in Electrical and Computer Engineering). Available from, DOI: 10.1007/978-3-319-75067-5_6

Author

Felfernig, Alexander ; Tintarev, Nava ; Tran, Thi Ngoc Trang ; Stettinger, Martin . / Explanations for Groups. Group Recommender Systems. Cham : Springer, 2018. pp. 105-126 (SpringerBriefs in Electrical and Computer Engineering).

BibTeX

@inbook{38862e6759bb4a8e80ea4e94dec827ca,
title = "Explanations for Groups",
abstract = "Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific goals such as increasing the transparency of a recommendation or increasing a user’s trust in the recommender system. In this chapter, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of aggregated predictions and aggregated models strategies.",
author = "Alexander Felfernig and Nava Tintarev and Tran, {Thi Ngoc Trang} and Martin Stettinger",
year = "2018",
doi = "10.1007/978-3-319-75067-5_6",
isbn = "978-3-319-75066-8",
series = "SpringerBriefs in Electrical and Computer Engineering",
publisher = "Springer",
pages = "105--126",
booktitle = "Group Recommender Systems",

}

RIS

TY - CHAP

T1 - Explanations for Groups

AU - Felfernig,Alexander

AU - Tintarev,Nava

AU - Tran,Thi Ngoc Trang

AU - Stettinger, Martin

PY - 2018

Y1 - 2018

N2 - Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific goals such as increasing the transparency of a recommendation or increasing a user’s trust in the recommender system. In this chapter, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of aggregated predictions and aggregated models strategies.

AB - Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific goals such as increasing the transparency of a recommendation or increasing a user’s trust in the recommender system. In this chapter, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of aggregated predictions and aggregated models strategies.

U2 - 10.1007/978-3-319-75067-5_6

DO - 10.1007/978-3-319-75067-5_6

M3 - Chapter

SN - 978-3-319-75066-8

T3 - SpringerBriefs in Electrical and Computer Engineering

SP - 105

EP - 126

BT - Group Recommender Systems

PB - Springer

ER -

ID: 36206922