Publikation: Intra-list similarity and human diversi...

Stammdaten
Titel: Intra-list similarity and human diversity perceptions of recommendations: the details matter
Untertitel:
Kurzfassung: AbstractThe diversity of the generated item suggestions can be an important quality factor of a recommender system. In offline experiments, diversity is commonly assessed with the help of the intra-list similarity (ILS) measure, which is defined as the average pairwise similarity of the items in a list. The similarity of each pair of items is often determined based on domain-specific meta-data, e.g., movie genres. While this approach is common in the literature, it in most cases remains open if a particular implementation of the ILS measure is actually a valid proxy for the human diversity perception in a given application. With this work, we address this research gap and investigate the correlation of different ILS implementations with human perceptions in the domains of movie and recipe recommendation. We conducted several user studies involving over 500 participants. Our results indicate that the particularities of the ILS metric implementation matter. While we found that the ILS metric can be a good proxy for human perceptions, it turns out that it is important to individually validate the used ILS metric implementation for a given application. On a more general level, our work points to a certain level of oversimplification in recommender systems research when it comes to the design of computational proxies for human quality perceptions and thus calls for more research regarding the validation of the corresponding metrics.
Schlagworte: Computer Science Applications, Human-Computer Interaction, Education
Publikationstyp: Beitrag in Zeitschrift (Autorenschaft)
Erscheinungsdatum: 12.12.2022 (Online)
Erschienen in: User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
zur Publikation
 ( Springer Verlag GmbH; )
Titel der Serie: -
Bandnummer: -
Heftnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: -

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Erscheinungsdatum: 12.12.2022
ISBN (e-book): -
eISSN: 1573-1391
DOI: http://dx.doi.org/10.1007/s11257-022-09351-w
Homepage: -
Open Access
  • Online verfügbar (Open Access)

Zuordnung
Organisation Adresse
Fakultät für Technische Wissenschaften
  Institut für Artificial Intelligence und Cybersecurity
Universitätsstr. 65-67
A-9020 Klagenfurt
Österreich
  -993705
   aics-office@aau.at
https://www.aau.at/en/aics/
zur Organisation
Universitätsstr. 65-67
AT - A-9020  Klagenfurt

Kategorisierung
Sachgebiete
  • 1020 - Informatik
Forschungscluster
  • Humans in the Digital Age
Zitationsindex
  • Science Citation Index Expanded (SCI Expanded)
Informationen zum Zitationsindex: Master Journal List
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • DECIDE (Decision-making in a digital environment)
  • Information Systems

Kooperationen
Organisation Adresse
Universität Utrecht
Domplein 29
9999 Utrecht
Niederlande
Domplein 29
NL - 9999  Utrecht

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