Long-term fairness for Group Recommender Systems with Large Groups

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10484963" target="_blank" >RIV/00216208:11320/22:10484963 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3523227.3547424" target="_blank" >https://doi.org/10.1145/3523227.3547424</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3523227.3547424" target="_blank" >10.1145/3523227.3547424</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Long-term fairness for Group Recommender Systems with Large Groups

Popis výsledku v původním jazyce

Group recommender systems (GRS) focus on recommending items to groups of users. GRS need to tackle the heterogeneity of group members&apos; preferences and produce recommendations of high overall utility while also considering some sense of fairness among group members. This work plans to aim for novel applications of GRS involving construction of large-scale groups of users and focusing on the long-term fairness of these groups which is in contrast with current research that concentrates on small groups of ephemeral nature. We believe that these directions could bring results of significant societal impact and scope of the effect expanding beyond currently considered GRS domains, e.g., helping to mitigate the filter bubble problem

Název v anglickém jazyce

Long-term fairness for Group Recommender Systems with Large Groups

Popis výsledku anglicky

Group recommender systems (GRS) focus on recommending items to groups of users. GRS need to tackle the heterogeneity of group members&apos; preferences and produce recommendations of high overall utility while also considering some sense of fairness among group members. This work plans to aim for novel applications of GRS involving construction of large-scale groups of users and focusing on the long-term fairness of these groups which is in contrast with current research that concentrates on small groups of ephemeral nature. We believe that these directions could bring results of significant societal impact and scope of the effect expanding beyond currently considered GRS domains, e.g., helping to mitigate the filter bubble problem

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

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 16th ACM Conference on Recommender Systems

ISBN

978-1-4503-9278-5

ISSN

—

e-ISSN

—

Počet stran výsledku

3

Strana od-do

724-726

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Seattle

Datum konání akce

18. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001139226600119