Cristin-resultat-ID: 1953363
Sist endret: 25. november 2021, 13:50
NVI-rapporteringsår: 2021
Resultat
Vitenskapelig artikkel
2021

Investigating the impact of recommender systems on user-based and item-based popularity bias

Bidragsytere:
  • Mehdi Elahi
  • Danial Khosh Kholgh
  • Mohammad Sina Kiarostami
  • Sorush Saghari
  • Shiva Parsa Rad og
  • Marko Tkalcic

Tidsskrift

Information Processing & Management
ISSN 0306-4573
e-ISSN 1873-5371
NVI-nivå 2

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2021
Publisert online: 2021
Volum: 58
Hefte: 5
Artikkelnummer: 102655

Importkilder

Scopus-ID: 2-s2.0-85107946260

Beskrivelse Beskrivelse

Tittel

Investigating the impact of recommender systems on user-based and item-based popularity bias

Sammendrag

Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular. In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score. The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages.

Bidragsytere

Aktiv cristin-person

Mehdi Elahi

  • Tilknyttet:
    Forfatter
    ved Institutt for informasjons- og medievitenskap ved Universitetet i Bergen

Danial Khosh Kholgh

  • Tilknyttet:
    Forfatter
    ved Ukjent institusjon

Mohammad Sina Kiarostami

  • Tilknyttet:
    Forfatter
    ved Oulun yliopisto

Sorush Saghari

  • Tilknyttet:
    Forfatter
    ved Ukjent institusjon

Shiva Parsa Rad

  • Tilknyttet:
    Forfatter
    ved Ukjent institusjon
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