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Long-term fairness for Group Recommender Systems with Large Groups

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Long-term fairness for Group Recommender Systems with Large Groups

  • Original language description

    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

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • 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 16th ACM Conference on Recommender Systems

  • ISBN

    978-1-4503-9278-5

  • ISSN

  • e-ISSN

  • Number of pages

    3

  • Pages from-to

    724-726

  • Publisher name

    ACM

  • Place of publication

    New York, NY, USA

  • Event location

    Seattle

  • Event date

    Sep 18, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    001139226600119