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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 1st Workshop on the Impact of Recommender Systems (ImpactRS 2019), part of the 13th ACM Conference on Recommender Systems (RecSys 2019 |
| Number of pages | 3 |
| Publication status | Published - 2019 |
Fields of science
- 202002 Audiovisual media
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102015 Information systems
JKU Focus areas
- Digital Transformation
Projects
- 1 Finished
-
Fein-granulare kultur-bezogene Musikempfehlungssysteme
Bauer, C. (PI)
01.02.2017 → 31.01.2020
Project: Funded research › FWF - Austrian Science Fund
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