User Perceptions of Diversity in Recommender Systems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

User Perceptions of Diversity in Recommender Systems

Popis výsledku v původním jazyce

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/.

Název v anglickém jazyce

User Perceptions of Diversity in Recommender Systems

Popis výsledku anglicky

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/.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA22-21696S" target="_blank" >GA22-21696S: Hluboké vizuální reprezentace nestrukturovaných dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

PROCEEDINGS OF THE 32ND ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, UMAP 2024

ISBN

979-8-4007-0433-8

ISSN

—

e-ISSN

—

Počet stran výsledku

11

Strana od-do

212-222

Název nakladatele

ASSOC COMPUTING MACHINERY

Místo vydání

NEW YORK

Místo konání akce

Cagliari

Datum konání akce

1. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001285444600024