User Perceptions of Diversity in Recommender Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10494543" target="_blank" >RIV/00216208:11320/24:10494543 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3627043.3659555" target="_blank" >https://doi.org/10.1145/3627043.3659555</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3627043.3659555" target="_blank" >10.1145/3627043.3659555</a>
Alternative languages
Result language
angličtina
Original language name
User Perceptions of Diversity in Recommender Systems
Original language description
In the context of recommender systems (RS), the concept of diversity is probably the most studied perspective beyond mere accuracy. Despite the extensive development of diversity measures and enhancement methods, the understanding of how users perceive diversity in recommendations remains limited. This gap hinders progress in multi-objective RS, as it challenges the alignment of algorithmic advancements with genuine user needs. Addressing this, our study delves into two key aspects of diversity perception in RS. We investigate user responses to recommendation lists generated using varied diversity metrics but identical diversification thresholds, and lists created with the same metrics but differing thresholds. Our findings reveal a user preference for metadata and content-based diversity metrics over collaborative ones. Interestingly, while users typically recognize more diversified lists as being more diverse in scenarios with significant diversification differences, this perception is not consistently linear and quickly diminishes when the diversification variance between lists is less pronounced. This study sheds light on the nuanced user perceptions of diversity in RS, providing valuable insights for the development of more user-centric recommendation algorithms. Study data and analysis scripts are available from https://osf.io/9y8gx/.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-21696S" target="_blank" >GA22-21696S: Deep Visual Representations of Unstructured Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
PROCEEDINGS OF THE 32ND ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, UMAP 2024
ISBN
979-8-4007-0433-8
ISSN
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e-ISSN
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Number of pages
11
Pages from-to
212-222
Publisher name
ASSOC COMPUTING MACHINERY
Place of publication
NEW YORK
Event location
Cagliari
Event date
Jul 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001285444600024