Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings

Christine Bauer; E. Zangerle

Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings

Christine Bauer
, E. Zangerle

Institut für Computational Perception

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not able to evaluate all relevant aspects in such a complex setting. We reason that employing a multi-method evaluation, where multiple evaluation methods or measures are combined and integrated, allows for getting a richer picture and prevents blind spots in the evaluation outcome.

Originalsprache	Englisch
Titel	Proceedings of the 1st Workshop on the Impact of Recommender Systems (ImpactRS 2019), part of the 13th ACM Conference on Recommender Systems (RecSys 2019
Seitenumfang	3
Publikationsstatus	Veröffentlicht - 2019

Wissenschaftszweige

202002 Audiovisuelle Medien
102 Informatik
102001 Artificial Intelligence
102003 Bildverarbeitung
102015 Informationssysteme

JKU-Schwerpunkte

Digital Transformation

Andere Dateien und Links

http://ceur-ws.org/Vol-2462/

1 Abgeschlossen

Fein-granulare kultur-bezogene Musikempfehlungssysteme
Bauer, C. (Projektleiter*in)
01.02.2017 → 31.01.2020
Projekt: Geförderte Forschung › FWF - Österreichischer Wissenschaftsfonds

Dieses zitieren

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title = "Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings",

abstract = "In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not able to evaluate all relevant aspects in such a complex setting. We reason that employing a multi-method evaluation, where multiple evaluation methods or measures are combined and integrated, allows for getting a richer picture and prevents blind spots in the evaluation outcome.",

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year = "2019",

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AU - Zangerle, E.

PY - 2019

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AB - In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not able to evaluate all relevant aspects in such a complex setting. We reason that employing a multi-method evaluation, where multiple evaluation methods or measures are combined and integrated, allows for getting a richer picture and prevents blind spots in the evaluation outcome.

UR - http://ceur-ws.org/Vol-2462/

M3 - Conference proceedings

BT - Proceedings of the 1st Workshop on the Impact of Recommender Systems (ImpactRS 2019), part of the 13th ACM Conference on Recommender Systems (RecSys 2019

ER -

Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings

Abstract

Wissenschaftszweige

JKU-Schwerpunkte

Andere Dateien und Links

Projekte

Fein-granulare kultur-bezogene Musikempfehlungssysteme

Dieses zitieren